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How to build AI applications users can trust
December 28, 2020 Big Data
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To work effectively, algorithms need user data — typically on an ongoing basis to help refine and improve the experience. To get user data, you need users. And to get users, especially lasting users who trust you with their data, you need to provide options that suit their comfort levels now while still allowing them to change them in future. In essence, to get user buy-in, you need a two-step approach: Let users know what data you want to collect and why, and give them control over the collection.
Step 1: Providing continuous transparency
The first step in finding the balance is to equip your users with knowledge. Users need to know what data is being collected and how that data is being used before they decide to engage with an application. Already, mounting pressure on the industry is steering the ship in this direction: Apple recently announced a privacy label for all of its applications that will promote greater awareness for users around what data is being collected when they use their apps. Microsoft's CaptionBot, below, is a good example of how to give users an easy-to-understand overview of what's happening with their data behind the scenes.
Microsoft's CaptionBot offers clear information about data storage, publication and usage as well as an easy-to-understand overview of the kinds of systems working behind the scenes to make the AI captioning tool work.
Above: Microsoft's CaptionBot offers clear information about data storage, publication and usage as well as an easy-to-understand overview of the kinds of systems working behind the scenes to make the AI captioning tool work.
Health app Ada, below, is an example of how to avert user confusion over data collection choices.
Above: Health app ada explains the logic behind its input selections at the outset, so users can understand how their inputs affect the application and its ability to perform the desired actions.
Not only does sharing this information upfront give users a sense of empowerment and help build trust with your experience over time, it also gives you an opportunity to help them understand how sharing their data can improve their experience — and how their experience will be diminished without that data. By arming users with information that helps them understand what happens when they share their data, we also arm them with the tools to understand how this exchange can benefit them, bolstering their excitement for using the app.
In addition to these details upfront, presenting users with information as they use the application is important. Sharing information about algorithm effectiveness (how likely the algorithm is to succeed at the task) and algorithm confidence (how certain the algorithm is in the results it produced) can make a big difference when it comes to user comfort in engaging with these technologies. And as we know, comfort plays a major part in adoption and engagement.
Consider the confidence ratings Microsoft offers in some of its products, below.
Above: When an algorithm is making a "best guess", displaying a confidence rating (in the first image using Microsoft's Bing Image Search, a rating between 0 and 1, and in the second from Microsoft's Celebs Like Me, a percentage rating) helps users understand how much trust they should place in the outcomes of the algorithm.
Users should be given insight into some of the operations and mechanics, too. So it's important to acknowledge when the mechanisms are at work or "thinking", or when there's been a hand-off from the algorithm to a human, or when data is being shared to third-party systems or stored for potential later applications. Continually offering up opportunities for building awareness and understanding about your application will lead to higher levels of trust with using it. And the more users trust it, the more likely they will be to continue to engage with it over time.
Step 2: Handing over control
Even when the benefits of an application are compelling enough for users to opt in, users don't necessarily want to use AI all the time. There may be circumstances when they want to withdraw from or limit the amount they engage with the technology. How can we empower them to choose the amount of AI they interact with at the moments that matter most? Again, a combination of upfront and semi-regular check-ins works well here.
When informing users about what data you're collecting and how it's being used, give them the chance to opt out of sharing certain types of data if the use case doesn't meet their needs. Where possible, present them with a graduated series of options — what you get when you enable all data sharing versus some versus none — to allow them to choose the option that makes the most sense for them.
Consider the example below from food-ordering app Ritual.
Above: Popular food ordering app Ritual allows users to opt of sharing certain data and also informs users of how opting out will impact the application's functionality.
Whenever you add a new product feature or a user engages with a feature for the first time, prompt them to look at or change their level of data sharing. What may not have seemed relevant to them before could be very compelling with a new use case presented. And if a new type of data is being collected, prompt them again.
One final way to offer up control: Give users the chance to direct the application. This can mean simply checking in with your users from time to time about what features they like, which ones they don't, and what they want from your application. Or, more importantly, it can be as a part of the application itself. Can users adjust the level of certain inputs to produce different results (e.g. weighting one input over another for a recommendation algorithm)? Can they go back a step or override certain aspects manually? Handing over the controls in as literal a sense as possible helps users feel empowered by the application instead of intimidated by it.
Youper's AI therapy app provides a good example of how to offer users control.
Above: Youper's AI therapy app doesn't require users to set all parameters at the outset but instead offers up regular opportunities for them to refine their experience as they continue to engage with the application (and explains why it may help them to do so).
Every application is different, and every approach to empowering users will be a little different as a result. But when you offer transparency into how and why your system is taking in the information that it is, and you give consumers the chance to opt out of sharing certain pieces of information, you create space for trust. And when your users trust you, they'll be more inclined to share the data you need to make your products and services come alive.
Jason Cottrell is Founder and CEO at Myplanet.
Erik von Stackelburg is CDO at Myplanet.
Big Data – VentureBeat
ClickDimensions Social Engagement – designed exclusively for Dynamics 365 users
December 21, 2020 CRM News and Info
Maximize Your Social ROI With ClickDimensions Social Engagement
ClickDimensions social engagement is a complete social media marketing platform designed exclusively for Dynamics users. This powerful platform is an add-on module to the ClickDimensions marketing automation solution. ClickDimensions social engagement platform goes way beyond the out of the box solution. ClickDimensions has partnered with social media solution vendor, Oktopost to bring users a way to effectively manage all their social media activity- including scheduling, publishing, analytics and much more. The social engagement platform is the perfect complement to the social marketing features available out of the box in ClickDimensions. The platform goes beyond the out-of-the-box posting functionalities.
ClickDimensions Social Engagement -designed specifically for B2B marketers
This user-friendly platform is designed specifically for B2B marketers who need smart, time saving solutions that maximize their social media ROI. The reason the social engagement platform is B2B rather than B2C is that every action taken within the tool is measurable/ So from post to click to conversion you can better understand your social media strategy, track conversions coming in through social, and better understand how social is affecting your ROI. The platform is segmented by campaigns for a couple reasons. One reason is that other marketing platforms are segmented by campaigns, so in order to keep with the flow, the ClickDimensions Social Engagement platform is also segmented by campaigns. The other reason this platform segments by campaigns is that it makes is easier to not only find your posts later on, but also to filter and find data.
There are 5 main components to the ClickDimensions social engagement platform – Social Publishing, Social Listening, Content Curation, Analytics, Viewing Data in CRM. I will briefly mention advocacy, as it is an add-on feature.
Upon logging in to the Social Engagement platform, you will be directed to the dashboard. From the dashboard, you can choose where you ant to go and what you want to do. The four icons along the left hand side navigation are publishing, streams, inbox, advocacy, and analytics. On the bottom of the left hand navigation, you'll find your settings (your face) and notifications.
Upon logging on, if there are any new updates to the platform, you will see a pop-up box on the lower left hand of the screen.
Social publishing allows you to schedule content in advance, letting the platform do the rest – spreading your posts across your schedule. It is easy to schedule posts, across networks, and from your marketing teams. There are two ways to create a new post – by clicking on the blue New Post button on the calendar or by going to the campaign and clicking on that same New Post button. You will notice that the pop up box is automatically separated into the three networks, Facebook, LinkedIn, and Twitter. (Instagram is listen only at this time). The reason it is segmented by network is so that you can follow best social media practice and customize your posts, according to the network.
To view your scheduled posts, go to the Calendar. From the calendar, you can do many things. You can see at a glance what is posting and to where in every campaign. You can also view individual reports from posts that have already done out. You can easily edit the day your scheduled posts is to go out by simply hovering over the network icon and dragging and dropping. You can also edit posts that have not gone out, by clicking on the blue Edit button. There you can edit text, image/video, date, time, or message.
Color coded campaigns make it easy to quickly see which campaign you are posting to.
Social listening is important to B2B marketers, as it allows us to stay on top of the relevant social conversations happening all around us. Not only does it allow for better engagement with our existing audience, it also helps us to find new connections. Streams are the platforms social listening component. Streams are updated in real-time and you can make as many streams as you like.
Streams enable you to easily monitor and engage in social conversations across multiple networks. Each social platform has different capabilities and functionalities. Streams allow you to cut through the social media noise and focus on the discussions that really matter. Within the stream, you can like, comment, or retweet –all from one platform!
Another nice feature of streams is the ability to create live monitors that track specific keywords, mentions, and company pages.
To learn more about ClickDimensions Social Engagement, visit our blog.
To get started with ClickDimensions Social Engagement, contact the professionals at enCloud9.
CRM Software Blog | Dynamics 365
Teachable AI will help Alexa users set up preferences
The cutting-edge computer architecture that's changing the AI game
Learn about the next-gen architecture needed to unlock the true capabilities of AI and machine learning.
Amazon today detailed a capability coming to Alexa called Teachable AI that will enable the assistant to ask questions to fill gaps in its understanding. First announced during the company's September virtual press event, Teachable AI leverages machine learning to determine whether a request can be a trigger for a teachable moment. If Alexa makes this determination, it will ask a customer for information to help it learn.
Amazon says Teachable AI will become available in the next few months for smart home devices before expanding to other areas.
Scientists at Amazon's Alexa AI research division have long pursued semi-supervised and unsupervised learning techniques, in which AI systems learn to make predictions without ingesting gobs of annotated data. Semi-supervised and unsupervised learning have their limitations, but both promise to supercharge Alexa and other voice assistants' capabilities by imbuing them with a humanlike capacity for inference.
In the case of Teachable AI, if a customer says something like, "Alexa, set the living room light to study mode," the assistant might now respond, "I don't know what study mode is, can you teach me?" Alexa will extract a definition from the customer's answer and when they later make the same (or a similar) request, reply with the learned action.
As another example, Alexa might not automatically know that a customer's preferred reading setting is 40% brightness when they say, "Alexa, set the light to [my] reading mode." Using Teachable AI, the assistant will ostensibly learn these definitions and corresponding actions and associate them with a particular account for future use.
"This is an exciting step forward not just for Alexa, but for all AI services that rely on end users to teach them," Alexa VP and head scientist Rohit Prasad wrote in a blog post. "I am excited to [see] … AI advancements bringing us closer to our long-term vision of making interactions with Alexa as simple as speaking to another person."
Teachable AI builds on latent goal inference, which enables devices powered by Alexa to infer goals implicit in requests but not directly expressed. For instance, if a user says "How long does it take to steep tea?" Alexa might follow up with "Five minutes is a good place to start" and the question "Would you like me to set a timer for five minutes?" Latent goal inference and Teachable AI also complement Natural Turn Taking, an Alexa feature that lets users converse with the assistant without having to repeat a wake word. (Three AI models run in parallel to power Natural Turn Taking, which will initially only be available in English when it launches sometime next year.)
A Hybrid Marketing Automation Option for Microsoft Dynamics Users
December 1, 2020 CRM News and Info
A hybrid marketing automation platform solution for Microsoft Dynamics merges two best-in-breed solutions to deliver outstanding performance, usability and flexibility. In this case, the emfluence Marketing Platform, a robust, mature marketing automation stack is merged with Microsoft Dynamics 365. The result is a dream solution that leverages the best of both worlds.
How they are connected.
The emfluence Marketing Platform is a SaaS platform that operates independently of third-party solutions. This means marketers can log directly into the system to create, deploy, and track multi-channel online marketing campaigns through email, landing pages, Facebook, Twitter, LinkedIn, online surveys and forms. In addition, marketers can create custom workflows and track interactions across channels to better market to their customers and prospects. Through the use of standard connectors and built in APIs, the emfluence Marketing Platform can be connected to systems that support third-party connectivity. The emfluence Marketing Platform excels at creating, deploying, and tracking marketing campaigns, but it isn't designed to be a full CRM.
The counterpart to the emfluence Marketing Platform in this hybrid solution is Microsoft Dynamics CRM, a robust and powerful solution for managing customer relationships. Microsoft Dynamics excels at customizability and management of customer data but doesn't provide the core functions that marketers demand to create, deploy, and track campaigns designed to engage with customers and prospects.
A hybrid solution leverages the advantages of both systems and creates seamless connectivity. In the case of the emfluence Marketing Platform solution for Microsoft Dynamics CRM, the integration embeds the entire emfluence user experience into Microsoft Dynamics and allows for single sign on while consuming only a single application user license. Users never need to leave their familiar CRM environment to fully leverage the power of an advanced marketing automation platform. Staying true to the promise of a hybrid solution, users can also access the emfluence Marketing Platform directly through the standard SaaS web platform.
Within CRM, only the table structures necessary to fulfil the promise of a seamless integration are created when the solution is installed. This means very little data redundancy. In addition, it helps control consumption where appropriate. Deploying millions of emails over time can result in significant data usage. Optionally, and also staying true to the promise of a hybrid solution, CRM instances can be configured to create table structure and collect data for activity and engagement data that may be necessary for running queries, segmenting data and creating downstream actions via Flows created in Power Apps. Much of this functionality can be accomplished from within the emfluence Marketing Platform, so the optional creation of the duplicate data set becomes a business decision on how an individual organization would prefer to operate.
Organizations that leverage this hybrid solution also realize the value of a user licensing advantage. There does not need to be a one to one relationship between users of Microsoft Dynamics CRM and the emfluence Marketing Platform. In most cases, user-based licensing in the emfluence Marketing Platform is not part of the fee structure. That means that there can be active users of the emfluence Marketing Platform that do not need Microsoft user licenses. This can be a significant cost consideration when establishing how your Marketing team will best execute important strategies and tactics.
For years, Microsoft Dynamics CRM customers have been limited to the choice of either a completely native solution or an independent solution with loose connectivity to Microsoft Dynamics CRM. With the emfluence Marketing Platform solution the best of breed worlds come together in an elegant and powerful application.
For more information or for a demo, reach out to us at here or learn more about our integration to Microsoft Dynamics here.
Stop Cherry Picking! Automate Assignment of Leads/Cases within Dynamics 365 CRM/ Power Apps to specific users!
October 13, 2020 CRM News and Info
Do you find it hard to manually allocate work items to your team in Dynamics 365 CRM/Power Apps? Are you looking for a better way to allot/assign work items in Dynamics 365 CRM/Power Apps?
Presenting to you, one of our Preferred Apps on Microsoft AppSource – Round Robin Routing & Assign – a productivity app that will help you to automatically assign work items in Dynamics 365 CRM/Power Apps.
So, what are the benefits of using Round Robin Routing & Assign?
Automatic Assignment of Work Items
With Round Robin Routing & Assign, you can automate the assignment of work items in Dynamics 365 CRM such as Leads, Cases, etc. in a systematic way. Basically, it applies the 'Round Robin' rule to allocate the work items to your team members. Suppose you are the Sales Manager and you have 4 Sales Representatives – Missy, Mike, Marcus & Linn – in your team to whom you have to allocate the incoming Leads. With Round Robin Routing & Assign app all the incoming Leads will be automatically assigned to your team members in the following manner – first Lead will go to Missy, second one to Mike, third to Marcus, fourth will go to Linn, fifth will again go to Missy and so on. This process will continue until all the Leads are assigned. With this automation, you can avoid cherry picking and will also not lose Leads due to mismanagement.
Fair & Even Distribution of Work Items
Another interesting feature of Round Robin Routing & Assign is that you can assign work items to your team members on the basis of their individual capacity. Suppose between the four Sales Representatives – Missy, Mike, Marcus & Linn – Missy is the most experienced, Mike is the rookie and both Marcus & Linn have just a few years of experience. Now depending upon their experience you can set a numerical value to their capacity like for Missy you can give 100, for Mike 40, for both Marcus & Linn 60. In the same way, you can set numerical value for work items. Here, you can set the value for work items as 10. Based on these values, Round Robin Routing & Assign will allot the work items on the basis of the Round Robin algorithm i.e. the first work item will be given to Missy, second to Mike and so on. So, after the first round of assignment, Missy's capacity will be reduced to 90, Mike's to 30 and both Marcus's & Linn's to 50. In this way, work items will be assigned sequentially to all. But once Mike's capacity is fulfilled it will continue to assign the work items to others following the same sequence. Next, when both Marcus's & Linn's capacity is fulfilled, it will only allot work items to Missy until it meets Missy's capacity. In this way, you can ensure fair and even distribution of workloads among your team members.
Analyze Performance
After automating the assignment of work items, you can check daily the dashboards to know how much work items (Lead) were assigned to your team members and how much is yet to be assigned. As soon as they complete the assigned workloads (closing the Leads), new ones will be assigned to them. This all will be reflected in your customized dashboards based on which you can analyze the performance of your team members.
Quite a handy automation app to have, isn't it?
So, don't wait around! Go to our website or Microsoft AppSource and test for yourself the benefits and features of this productivity app for a FREE period of 15 days.
You can also explore our other Preferred Apps for Dynamics 365 CRM/Power Apps.
Feel free to mail us at crm@inogic.com for any automation queries and personal demo on any of our Preferred Apps.
Until then – Stay Safe, Stay Healthy!
Alexa users can now buy gas with a voice command at some stations
September 1, 2020 Big Data
Automation and Jobs
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In partnership with ExxonMobil and fintech provider Fiserv, Amazon today launched an Alexa feature that lets users pay for gas with a voice command. The integration was first announced in January at the 2020 Consumer Electronics Show, but today marks its rollout at over 11,500 Exxon and Mobil stations across the U.S.
Starting today, customers with Alexa-enabled vehicles, Alexa-enabled devices like Echo Auto and Echo Buds, or the Alexa app on Android or iOS can say "Alexa, pay for gas" to have Alexa confirm their station location and pump number. Confirming the location and number will activate the pump, after which customers can optionally select the fuel grade and begin fueling.
Amazon says transactions are made through Amazon Pay and powered by Fiserv, which handles things like geolocation at Exxon and Mobil stations, pump activation, payment processing, and payment tokenization. Gas payments will default to the primary payment method associated with customers' accounts (excepting Amazon gift cards), but Exxon notes on its website that there's currently no way to earn Exxon Mobile Rewards+ points.
Amazon says access to the new feature — which doesn't work with Echo devices "built for at-home use," including the Echo, Echo Show, and Echo Dot — can be secured with a voice PIN if customers choose. This can be set up through the Account Settings screen in the Alexa app. The company also notes that the name, email address, payment, location, and other Amazon account information required to support the transaction will be shared with ExxonMobil at payment time.
The launch of Alexa-powered payments at ExxonMobil stations comes after Amazon debuted partnerships allowing users in some countries — including the U.S. and India — to pay utility companies with information stored in their Amazon accounts. Coinciding with the launch, a complementary bill management feature rolled out to Alexa-enabled devices in the U.S.
Adding New Users to Allow Access to Power Apps Portals Admin Center
July 31, 2020 Microsoft Dynamics CRM
A common issue for new Power Apps Portals deployments is that you may need to have multiple internal employees that need admin level access to the Power Apps Portals admin center. These users would need this level of access to perform common Portal Actions, including: Restart the portal Update Dynamics 365 URL Install Project Service Automation Extension Install Field Service extension for partner…
PowerObjects- Bringing Focus to Dynamics CRM
Google's AI tool lets users trigger mobile app actions with natural language instructions
July 11, 2020 Big Data
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Google is investigating ways AI might be used to ground natural language instructions to smartphone app actions. In a study accepted to the 2020 Association for Computational Linguistics (ACL) conference, researchers at the company propose corpora to train models that would alleviate the need to maneuver through apps, which could be useful for people with visual impairments.
When coordinating efforts and accomplishing tasks involving sequences of actions — for example, following a recipe to bake a birthday cake — people provide each other with instructions. With this in mind, the researchers set out to establish a baseline for AI agents that can help with similar interactions. Given a set of instructions, these agents would ideally predict a sequence of app actions as well as the screens and interactive elements produced as the app transitions from one screen to another.
In their paper, the researchers describe a two-step solution comprising an action-phrase extraction step and a grounding step. Action-phrase extraction identifies the operation, object, and argument descriptions from multi-step instructions using a Transformer model. (An "area attention" module within the model allows it to attend to a group of adjacent words in the instruction as a whole for decoding a description.) Grounding matches the extracted operation and object descriptions with a UI object on the screen, again using a Transformer model but one that contextually represents UI objects and grounds object descriptions to them.
Above: The action-phrase extraction model takes a word sequence of a natural language instruction and outputs a sequence of spans (denoted in red boxes) that indicate the phrases describing the operation, the object, and the argument of each action in the task.
The coauthors created three new data sets to train and evaluate their action-phrase extraction and grounding model:
The first contains 187 multi-step English instructions for operating Pixel phones along with their corresponding action-screen sequences.
The second contains English "how-to" instructions from the web and annotated phrases that describe each action.
The third contains 295,000 single-step commands to UI actions covering 178,000 UI objects across 25,000 mobile UI screens from a public Android UI corpus.
They report that a Transformer with area attention obtains 85.56% accuracy for predicting span sequences that completely match the ground truth. Meanwhile, the phrase extractor and grounding model together obtain 89.21% partial and 70.59% complete accuracy for matching ground-truth action sequences on the more challenging task of mapping language instructions to executable actions end-to-end.
The researchers assert that the data sets, models, and results — all of which which are available on open source on GitHub — provide an important first step on the challenging problem of grounding natural language instructions to mobile UI actions.
"This research, and language grounding in general, is an important step for translating multi-stage instructions into actions on a graphical user interface. Successful application of task automation to the UI domain has the potential to significantly improve accessibility, where language interfaces might help individuals who are visually impaired perform tasks with interfaces that are predicated on sight," Google Research scientist Yang Li wrote in a blog post. "This also matters for situational impairment when one cannot access a device easily while encumbered by tasks at hand."
Amazon's new AI technique lets users virtually try on outfits
June 5, 2020 Big Data
In a series of papers scheduled to be presented at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Amazon researchers propose complementary AI algorithms that could form the foundation of an assistant that helps customers shop for clothes. One lets people fine-tune search queries by describing variations on a product image, while another suggests products that go with items a customer has already selected. Meanwhile, a third synthesizes an image of a model wearing clothes from different product pages to demonstrate how items work together as an outfit.
Amazon already leverages AI to power Style by Alexa, a feature of the Amazon Shopping app that suggests, compares, and rates apparel using algorithms and human curation. With style recommendations and programs like Prime Wardrobe, which allows users to try on clothes and return what they don't want to buy, the retailer is vying for a larger slice of sales in a declining apparel market while surfacing products that customers might not normally choose. It's a win for businesses on its face — excepting cases where the recommended accessories are Amazon's own, of course.
Virtual try-on network
Researchers at Lab126, the Amazon hardware lab which spawned products like Fire TV, Kindle Fire, and Echo, developed an image-based virtual try-on system called Outfit-VITON that's designed to help visualize how clothing items in reference photos might look on an image of a person. It can be trained on a single picture using a generative adversarial network (GAN), Amazon says, a type of model with a component called a discriminator that learns to distinguish generated items from real images.
"Online apparel shopping offers the convenience of shopping from the comfort of one's home, a large selection of items to choose from, and access to the very latest products. However, online shopping does not enable physical try-on, thereby limiting customer understanding of how a garment will actually look on them," the researchers wrote. "This critical limitation encouraged the development of virtual fitting rooms, where images of a customer wearing selected garments are generated synthetically to help compare and choose the most desired look."
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Outfit-VITON comprises several parts: a shape generation model whose inputs are a query image, which serves as the template for the final image; and any number of reference images, which depict clothes that will be transferred to the model from the query image.
In preprocessing, established techniques segment the input images and compute the query person's body model, representing their pose and shape. The segments selected for inclusion in the final image pass to the shape generation model, which combines them with the body model and updates the query image's shape representation. This shape representation moves to a second model — the appearance generation model — that encodes information about texture and color, producing a representation that's combined with the shape representation to create a photo of the person wearing the garments.
Outfit-VITON's third model fine-tunes the variables of the appearance generation model to preserve features like logos or distinctive patterns without compromising the silhouette, resulting in what Amazon claims is "more natural" outputs than those of previous systems. "Our approach generates a geometrically correct segmentation map that alters the shape of the selected reference garments to conform to the target person," the researchers explained. "The algorithm accurately synthesizes fine garment features such as textures, logos, and embroidery using an online optimization scheme that iteratively fine-tunes the synthesized image."
Visiolinguistic product discovery
One of the other papers tackles the challenge of using text to refine an image that matches a customer-provided query. The Amazon engineers' approach fuses textual descriptions and image features into representations at different levels of granularity, so that a customer can say something as abstract as "Something more formal" or as precise as "Change the neck style," and it preserves some image features while following customers' instructions to change others.
The system consists of models trained on triples of inputs: a source image, a textual revision, and a target image that matches the revision. The inputs pass through three different sub-models in parallel, and at distinct points in the pipeline, the representation of the source image is fused with the representation of text before it's correlated with the representation of the target image. Because the lower levels of the model tend to represent lower-level input features (e.g., textures and colors) and higher levels higher-level features (sleeve length or tightness of fit), hierarchical matching helps to train the system to ensure it's able to handle textual modifications of different resolutions, according to Amazon.
Each fusion of linguistic and visual representations is performed by a separate two-component model. One uses a joint attention mechanism to identify visual features that should be the same in the source and target images, while the other identifies features that should change. In tests, the researchers say that it helped to find valid matches to textual modifications 58% more frequently than its best-performing predecessor.
"Image search is a fundamental task in computer vision. In this work, we investigate the task of image search with text feedback, which entitles user to interact with the system by selecting a reference image and providing additional text to refine or modify the retrieval results," the coauthors wrote. "Unlike the prior works that mostly focus on one type of text feedback, we consider the more general form of text, which can be either attribute-like description, or natural language expression."
Complementary-item retrieval
The last paper investigates a technique for large-scale fashion data retrieval, where a system predicts an outfit item's compatibility with other clothing, wardrobe, and accessory items. It takes as inputs any number of garment images together with a numerical representation called a vector indicating the category of each, along with a category vector of the customer's sought-after item, allowing a customer to select things like shirts and jackets and receive recommendations for shoes.
"Customers frequently shop for clothing items that fit well with what has been selected or purchased before," the researchers wrote. "Being able to recommend compatible items at the right moment would improve their shopping experience … Our system is designed for large-scale retrieval and outperforms the state-of-the-art on compatibility prediction, fill-in-the-blank, and outfit complementary item retrieval."
Above: An example of outfit complementary item retrieval. First
row: partial outfit with a missing item (shoe). Second row: retrieved results using the authors' method.
Image Credit: Amazon
Images pass through a model that produces a vector representation of each, and each representation passes through a set of masks that de-emphasize some representation features and amplify others. (The masks are learned during training, and the resulting representations encode product information like color and style that's relevant only to a subset of complementary items, such as shoes, handbags, and hats.) Another model takes as input the category for each image and the category of the target item and outputs values for prioritizing the masks, which are called subspace representations.
The whole system is trained using an evaluation criterion that accounts for the outfit. Each training sample includes an outfit as well as items that go well with that outfit and a group of items that don't, such that post-training, the system produces vector representations of every item in a catalog. Finding the best complement for a particular outfit then becomes a matter of looking up the corresponding vectors.
In tests that use two standard measures on garment complementarity, the system outperformed its three top predecessors with 56.19% fill-in-the-blank accuracy (and 87% compatibility area under the curve) while enabling more efficient item retrieval, and while achieving state-of-the-art results on data sets crawled from multiple online shopping websites (including Amazon and Like.com).
AI tools could improve fake news detection by analyzing users' interactions and comments
In a paper published on the preprint server Arxiv.org, researchers affiliated with Microsoft and Arizona State University propose an approach to detecting fake news that leverages a technique called weak social supervision. They say that by enabling the training of fake news-detecting AI even in scenarios where labeled examples aren't available, weak social supervision opens the door to exploring how aspects of user interactions indicate news might be misleading.
According to the Pew Research Center, approximately 68% of U.S. adults got their news from social media in 2018 — which is worrisome considering misinformation about the pandemic continues to go viral, for instance. Companies from Facebook and Twitter to Google are pursuing automated detection solutions, but fake news remains a moving target owing to its topical and stylistic diverseness.
Building on a study published in April, the coauthors of this latest work suggest that weak supervision — where noisy or imprecise sources provide data labeling signals — could improve fake news detection accuracy without requiring fine-tuning. To this end, they built a framework dubbed Tri-relationship for Fake News (TiFN) that models social media users and their connections as an "interaction network" to detect fake news.
Interaction networks describe the relationships among entities like publishers, news pieces, and users; given an interaction network, TiFN's goal is to embed different types of entities, following from the observation that people tend to interact with like-minded friends. In making its predictions, the framework also accounts for the fact that connected users are more likely to share similar interests in news pieces; that publishers with a high degree of political bias are more likely to publish fake news; and that users with low credibility are more likely to spread fake news.
To test whether TiFN's weak social supervision could help to detect fake news effectively, the team validated it against a Politifact data set containing 120 true news and 120 verifiably fake pieces shared among 23,865 users. Versus baseline detectors that consider only news content and some social interactions, they report that TiFN achieved between 75% to 87% accuracy even with a limited amount of weak social supervision (within 12 hours after the news was published).
In another experiment involving a separate custom framework called Defend, the researchers sought to use as a weak supervision signal news sentences and user comments explaining why a piece of news is fake. Tested on a second Politifact data set consisting of 145 true news and 270 fake news pieces with 89,999 comments from 68,523 users on Twitter, they say that Defend achieved 90% accuracy.
[W]ith the help of weak social supervision from publisher-bias and user-credibility, the detection performance is better than those without utilizing weak social supervision. We [also] observe that when we eliminate news content component, user comment component, or the co-attention for news contents and user comments, the performances are reduced. [This] indicates capturing the semantic relations between the weak social supervision from user comments and news contents is important," wrote the researchers. "[W]e can see within a certain range, more weak social supervision leads to a larger performance increase, which shows the benefit of using weak social supervision."
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