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Baidu details its adversarial toolbox for testing robustness of AI models

January 18, 2020 Big Data

No matter the claimed robustness of AI and machine learning systems in production, none are immune to adversarial attacks, or techniques that attempt to fool algorithms through malicious input. It’s been shown that generating even small perturbations on images can fool the best of classifiers with high probability. And that’s problematic considering the wide proliferation of the “AI as a service” business model, where companies like Amazon, Google, Microsoft, Clarifai, and others have made systems that might be vulnerable to attack available to end users.

Researchers at tech giant Baidu propose a partial solution in a recent paper published on Arxiv.org: Advbox. They describe it as an open source toolbox for generating adversarial examples, and they say it’s able to fool models in frameworks like Facebook’s PyTorch and Caffe2, MxNet, Keras, Google’s TensorFlow, and Baidu’s own PaddlePaddle.

While the Advbox itself isn’t new — the initial release was over a year ago — the paper dives into revealing technical detail.

AdvBox is based on Python, and it implements several common attacks that perform searches for adversarial samples. Each attack method uses a distance measure to quantify the size of adversarial perturbation, while a sub-model — Perceptron, which supports image classification and object detection models as well as cloud APIs — evaluates the robustness of a model to noise, blurring, brightness adjustments, rotations, and more.

AdvBox ships with tools for testing detection models susceptible to so-called adversarial t-shirts or facial recognition attacks. Plus, it offers access to Baidu’s cloud-hosted deepfakes detection service via an included Python script.

“Small and often imperceptible perturbations to [input] are sufficient to fool the most powerful [AI],” wrote the coauthors. “Compared to previous work, our platform supports black box attacks … as well as more attack scenarios.”

Baidu isn’t the only company publishing resources designed to help data scientists defend from adversarial attacks. Last year, IBM and MIT released a metric for estimating the robustness of machine learning and AI algorithms called Cross Lipschitz Extreme Value for Network Robustness, or CLEVER for short. And in April, IBM announced a developer kit called the Adversarial Robustness Toolbox, which includes code for measuring model vulnerability and suggests methods for protecting against runtime manipulation. Separately, researchers at the University of Tübingen in Germany created Foolbox, a Python library for generating over 20 different attacks against TensorFlow, Keras, and other frameworks.

But much work remains to be done. According to Jamal Atif, a professor at the Université Paris-Dauphine, the most effective defense strategy in the image classification domain — augmenting a group of photos with examples of adversarial images — at best has gotten accuracy back up to only 45%. “This is state of the art,” he said during an address in Paris at the annual France is AI conference hosted by France Digitale. “We just do not have a powerful defense strategy.”

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Big Data – VentureBeat

Tencent details how its MOBA-playing AI system beats 99.81% of human opponents

December 25, 2019 Big Data

In August, Tencent announced it had developed an AI system capable of defeating teams of pros in a five-on-five match in Honor of Kings (or Arena of Valor, depending on the region). This was a noteworthy achievement — Honor of Kings occupies the video game subgenre known as multiplayer online battle arena games (MOBAs), which are incomplete information games in the sense that players are unaware of the actions other players choose. The endgame, then, isn’t merely AI that achieves Honor of Kings superhero performance, but insights that might be used to develop systems capable of solving some of society’s toughest challenges.

A paper published this week peels back the layers of Tencent’s technique, which the coauthors describe as “highly scalable.” They claim its novel strategies enable it to explore the game map “efficiently,” with an actor-critic architecture that self-improves over time.

As the researchers point out, real-time strategy games like Honor of Kings require highly complex action control compared with traditional board games and Atari games. Their environments also tend to be more complicated (Honor of Kings has 10^600 possible states and and 10^18,000 possible actions) and the objectives more complex on the whole. Agents must not only learn to plan, attack, and defend but also to control skill combos, induce, and deceive opponents, all while contending with hazards like creeps and fully automated turrets.

Tencent’s architecture consists of four modules: Reinforcement Learning (RL) Learner, Artificial Intelligence (AI) Server, Dispatch Module, and Memory Pool.

The AI Server — which runs on a single processor core, thanks to some clever compression — dictates how the AI model interacts with objects in the game environment. It generates episodes via self-play, and, based on the features it extracts from the game state, it predicts players’ actions and forwards them to the game core for execution. The game core then returns the next state and the corresponding reward value, or the value that spurs the model toward certain Honor of Kings goals.

As for the Dispatch Module, it’s bundled with several AI Servers on the same machine, and it collects data samples consisting of rewards, features, action probabilities, and more before compressing and sending them to Memory Pools. The Memory Pool — which is also a server — supports samples of various lengths and data sampling based on the generated time, and it implements a circular queue structure that performs storage operations in a data-efficient fashion.

Lastly, the Reinforcement Learner, a distributed training environment, accelerates policy updates with the aforementioned actor-critic approach. Multiple Reinforcement Learners fetch data in parallel from Memory Pools, with which they communicate using shared memory. One mechanism (target attention) helps with enemy target selection, while another — long short-term memory (LSTM), an algorithm capable of learning long-term dependencies — teaches hero players skill combos critical to inflicting “severe” damage.

The Tencent researchers’ system encodes image features and game state information such that each unit and enemy target is represented numerically. An action mask cleverly incorporates prior knowledge of experienced human players, preventing the AI from attempting to traverse physically “forbidden” areas of game maps (like challenging terrain).

In experiments, the paper’s coauthors ran the framework across a total of 600,000 cores and 1,064 graphics cards (a mixture of Nvidia Tesla P40s and Nvidia V100s), which crunched 16,000 features containing unconcealed unit attributes and game information. Training one hero required 48 graphics cards and 18,000 processor cores at a speed of about 80,000 samples per second per card. And collectively for every day of training, the system accumulated the equivalent of 500 years of human experience.

The AI’s Elo score, derived from a system for calculating the relative skill levels of players in zero-sum games, unsurprisingly increased steadily with training, the coauthors note. It became relatively stable within 80 hours, according to the researchers, and within just 30 hours it began to defeat the top 1% of human Honor of Kings players.

The system executes actions via the AI model every 133 milliseconds, or about the response time of a top amateur player. Five professional players — “QGhappy.Hurt,” “WE.762,” “TS.NuanYang,” “QGhappy.Fly,” and “eStarPro.Ca,” — were invited to play against it, as well as a “diversity” of players attending the ChinaJoy 2019 conference in Shanghai between August 2 and August 5.

The researchers note that despite eStarPro.Cat’s prowess with mage-type heroes, the AI achieved five kills per game and was killed only 1.33 times per game on average. In public matches, its win rate was 99.81% over 2,100 matches, and five of the eight AI-controlled heroes managed a 100% win rate.

They’re far from the only ones whose AI beat human players — DeepMind’s AlphaStar beat 99.8% of human StarCraft 2 players, while OpenAI Five’s OpenAI Five framework defeated a professional team twice in public matches.

The Tencent researchers say that they plan to make both their framework and algorithms open source in the near future, toward the goal of fostering research on complex games like Honor of Kings.

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Facebook details the AI technology behind Instagram Explore

November 25, 2019 Big Data

According to Facebook, over half of Instagram’s roughly 1 billion users visit Instagram Explore to discover videos, photos, livestreams, and Stories each month. Predictably, building the underlying recommendation engine — which curates the billions of pieces of content uploaded to Instagram — posed an engineering challenge, not least because it works in real time.

In a blog post published this morning, Facebook for the first time peeled back the curtains on Explore’s inner workings. Its three-part ranking funnel, which the company says was architected with a custom query language and modeling techniques, extracts 65 billion features and makes 90 million model predictions every second. And that’s just the tip of the iceberg.

Tools

Before the team behind Explore embarked on building a content recommendation system, they developed tools to conduct large-scale experiments and obtain strong signals on the breadth of users’ interests. The first of these was IGQL, a meta language that provided the level of abstraction needed to assemble candidate algorithms in one place.

IGQL is optimized in C++, which helps minimize latency and compute resources without sacrificing extensibility, Facebook says. It’s both statistically validated and high-level, enabling engineers to write recommendation algorithms in a “Python-like” fashion. And it complements an account embeddings component that helps identify topically similar profiles as part of a retrieval pipeline that focuses on account-level information.

Facebook details the AI technology behind Instagram Explore

Above: Demonstration of ig2vec predicting account similarity.

Image Credit: Facebook

A framework — Ig2vec — treats Instagram accounts a user interacts with as word sequences in a sentence, which informs the predictions of a model with respect to which accounts the user might interact with. (Facebook notes that a sequence of accounts interacted with in a session is more likely to be topically coherent compared with random accounts.) Concurrently, Facebook’s AI Similarity Search nearest neighbor retrieval library (FAISS) queries millions of accounts based on a metric used in embedding training.

A classifier system is trained to predict a topic for a set of accounts based solely on the embedding, which when compared with human-labeled topics makes evident how well the embeddings capture topical similarity. It’s an important step, because retrieving accounts similar to those a user has expressed interest in helps narrow down a per-profile ranking inventory.

Ranking accounts in Explore based on interests necessitated predicting the most relevant content for each person, according to Facebook, and gave rise to a lightweight ranking distillation model that preselects candidates before passing them to complex ranking models. Using knowledge in the form of input candidates with features and outputs from the more complicated models, the simpler model tries to approximate the main ranking models as much as possible via direct (and indirect) learning.

Building Explore

Explore consists of two main stages, according to the team that designed it: the candidate generation stage (also known as the sourcing stage) and the ranking stage.

During the candidate generation stage, Explore taps accounts that users have interacted with previously to identify “seed accounts” of interest. They’re only a fraction of the accounts about the same interest, but they help identify topically similar accounts when combined with the above-mentioned embeddings.

Knowing the accounts that might appeal to a user is the first step toward sussing out which content might float their boat. IGQL allows different candidate sources to be represented as distinct subqueries, and this enables Explore to find tens of thousands of eligible candidates for the average person across many types of sources.

Above: This graphic shows a typical source for Instagram Explore recommendations.

Image Credit: Facebook

To ensure the recommended content remains safe and appropriate for users of all ages, signals are used to filter out anything that might not be eligible. Algorithms detect and filter spam and other content, typically before an inventory is built for each user.

Those filtering systems are quite effective, if Facebook’s latest Community Standards Enforcement Report is any indication. The network says that 845,000 pieces of content relating to self-injury and self-harm were removed in Q3 2019, of which 79.1% were detected proactively, and that over 99% of child nudity and exploitation posts were deleted over the past four quarters.

For every Explore ranking request, 500 candidates are selected from the thousands sampled and are passed along to the ranking stage. It’s there that they encounter a three-part infrastructure intended to balance relevance with computation efficiency.

In the first pass of the ranking stage, a distillation model mimics the combination of the other stages with a minimal number of features. It picks the 150 highest-quality and most relevant candidates out of the 500, after which a model with a full dense set of features (in the second phase) selects the top 50 candidates. Lastly, another model with a full set of features chooses the best 25 candidates, which populate the Explore grid.

Above: An illustration of the current final-pass model architecture.

Image Credit: Facebook

Sometimes the first-pass distillation model mimics the other two stages in ranking order. The fix is a multi-task, multi-layer algorithm that captures signals to predict actions people might take on content, from positive actions such as tapping Like or Favorite to negative actions like tapping the See Fewer Posts Like This button. The predictions are combined using a value model formula to capture prominence, after which a weighted sum determines whether the importance of a person saving a post, say, is higher than their liking a post.

In the interest of maintaining a “rich balance” between new content and existing content, the Explore team incorporated a rule into the aforementioned value model that boosts content diversity. It downranks posts from the same author or seed account by adding a penalty factor so users don’t see multiple posts from the same person or the same seed account in Explore.

“We rank the most relevant content based on the final value model score of each ranking candidate in a descendant way,” wrote the blog authors. “One of the most exciting parts of building Explore is the ongoing challenge of finding new and interesting ways to help our community discover the most interesting and relevant content on Instagram. We’re continuously evolving Instagram Explore, whether by adding media formats like Stories [or] entry points to new types of content, such as shopping posts and IGTV videos.”

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It’s All In The Details: Some Thoughts For Executives In The Entertainment Industry

July 17, 2019 BI News and Info

In the first blog of this series, I discussed the entertainment industry’s mandate to hold their customers’ attention. In my second blog, I talked about the importance of creating experiences that extend beyond the entertainment venue. In particular, I mentioned Walt Disney’s genius for extending familiar stories into films and then into theme park experiences. However, perfecting the idea of an extended experience is an ongoing process, not just for Disney, but for everyone in the entertainment industry. Now I’ll reveal the secret for creating a very personal and memorable experience: “specificity.”

For example, when Disneyland opened in 1955, the thematic areas of their park were given rather generic names (Main Street USA, Adventureland, Frontierland, Fantasyland, and Tomorrowland). They did have some dark rides that were tied to specific films, like Snow White’s Adventure and Peter Pan’s Flight, but most of the attractions were also generic in nature, like Autotopia, Storybook Land Canal Boats, and Frontierland Shootin’ Exposition. Ultimately, Disney added some specificity to some of its nonspecific attractions, like Haunted Mansion, Pirates of the Caribbean, and Jungle Cruise, when they went the other direction and became films.

Needless to say, Disney has learned a lot about creating experiences in the last 65 years. The original theory was that generic concepts would appeal to a wider audience, but that turns out not to be the case. In practice, people are drawn to richly detailed experiences because it gives them more potential ways to relate. For example, look at the new Star Wars: Galaxy’s Edge, which opened recently at Disneyland in California and is opening soon at Walt Disney World in Florida. Disney has learned that the more detailed and immersive an experience, the more personal it will be for patrons and the more likely it is to attract repeat business.

Of course, other parks have learned this lesson, too. For example, Bricksburg at Legoland in Florida; Angry Birds Land at Särkänniemi in Tampere, Finland and Thorpe Park in Surrey, England; Sesame Place at SeaWorld in Florida; and of course, The Wizardly World of Harry Potter at Universal Studios in Florida and California. Theme park executives know if they can leverage intellectual property that customers have been exposed to outside the park, it will create a more meaningful experience inside the park because customers will be immersed in a very specific and familiar world.

Casino resorts also offer these rich, immersive adventures. Las Vegas has experiences that range from the ultra-kitsch to the truly opulent. You’ll find Paris, Venice, New York, and ancient Greece within five miles of each other, and casinos use intellectual property to draw players to slot machines. Today, the slots are more interactive than ever and often have dynamic slot symbols moving about the screens. But the biggest experiential distinction among slot machines is branding. Would you rather play Game of Thrones, Kiss, Pac-Man, Wizard of Oz, or the all-time favorite Wheel of Fortune machine? One machine, TMZ, immerses patrons in a personalized experience by taking their picture and using it as a slot symbol.

Just like cinemas and entertainment parks, casino resorts need to hold a patron’s attention, but casinos hold their attention with fantasy. People come to resorts with the dream of not only beating the odds but living like the truly wealthy in luxurious rooms surrounded by sumptuous pools, fountains, and gardens. They’re thrilled by close encounters with beautiful showgirls or their favorite musical performer, and they delight when magicians or acrobats are able to do the seemingly impossible.

Cirque du Soleil is an excellent example of this. It has more than 25 visually mesmerizing shows running around the globe. Seven of them are running in Las Vegas alone and two leverage familiar intellectual property (The Beatles Love and Michael Jackson One). And all the shows are created with extraordinary detail and specificity that delivers a personal, memorable experience. In fact, ever on the cutting edge, Cirque du Soleil has encouraged the use of cellphones and a cloud-based app during its performances of Toruk to improve audience participation.

BrendaEntertainmentBlogOneQuote 1024x576 It’s All In The Details: Some Thoughts For Executives In The Entertainment Industry

The future of technology-infused entertainment is limited only by the imagination.

Currently, Cirque du Soleil has more than 4,000 employees, but it wasn’t always that way. It began in 1984 with only 20 performers, a leaky tent, and considerable debt. The success of Cirque du Soleil is not due only to its attention to detail on stage but its exhaustive, detailed efforts behind the scenes, as well. For a great look at how that happens, check out its truly amazing, behind-the-scenes videos about its production of Ka.

In fact, it’s quite ironic that Las Vegas and other bastions of entertainment have a reputation for a reckless “anything goes” reputation. The reality is that these places are subject to extraordinary regulation and must be able to assure the health and safety of their guests and employees. Only an intelligent enterprise that has complete control of its licensing and regulations, as well as an adequate, certifiably qualified workforce, and the ability to procure the best possible health and safety equipment, can be certain of that.

Further, if casino resorts want to provide their customers with unique experiences, they obviously need the ability to treat each customer uniquely. People come to these resorts for many different reasons. It might be to gamble, but it could be for a convention, a reunion, a wedding, or just for the shows. Patrons might be regular high-rollers, casual visitors, or travelers looking for a once-in-a-lifetime experience, and they should all be treated distinctively. While this probably means gathering significant data about these customers, it’s also critical to assure them that their information will be safely guarded, as casinos take confidentially and customer data security very seriously.

Experiences don’t just happen, they’re created. It’s called “experience management” (XM), and there is no industry that requires zealous experience management more than the entertainment industry. Whether you’re a cinema, museum, concert venue, casino, amusement park, Renaissance fair, or circus, if you want to (figuratively) go from a leaky tent and handful of performers to a billion-dollar business, you need more than a C-suite; you need an X-suite – a group of executives who are focused on the experience of their customers.

Can I honestly say that every startup entertainment enterprise will become wildly successful strictly by focusing on experience management? No. But I can assure you that all highly successful entertainment enterprises are putting considerable focus on their experience management or they’re on the path to obsolescence.

For an in-depth look at how the x-suite is changing the way companies do business, read “Meet the ‘X-Suite’ – the job roles shaping Experience Management” by Qualtrics, an SAP company.

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MIT CSAIL details technique for shrinking neural networks without compromising accuracy

May 7, 2019 Big Data

Deep neural networks — layers of mathematical functions modeled after biological neurons — are a versatile type of AI architecture capable of performing tasks from natural language processing to computer vision. That doesn’t mean that they’re without limitations, however. Deep neural nets are often quite large and require correspondingly large corpora, and training them can take days on even the priciest of purpose-built hardware.

But it might not have to be that way. In a new study (“The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks“) published by scientists at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), deep neural networks are shown to contain subnets that are up to 10 times smaller than the entire network, but which are capable of being trained to make equally precise predictions, in some cases more quickly than the originals.

The work is scheduled to be presented at the International Conference on Learning Representations (ICLR) in New Orleans, where it was named one of the conference’s top two papers out of roughly 1,600 submissions.

“If the initial network didn’t have to be that big in the first place, why can’t you just create one that’s the right size at the beginning?” said PhD student and coauthor Jonathan Frankle in a statement. “With a neural network you randomly initialize this large structure, and after training it on a huge amount of data it magically works. This large structure is like buying a big bag of tickets, even though there’s only a small number of tickets that will actually make you rich. But we still need a technique to find the winners without seeing the winning numbers first.”

MIT CSAIL details technique for shrinking neural networks without compromising accuracy

Above: Finding subnetworks within neural networks.

Image Credit: MIT CSAIL

The researchers’ approach involved eliminating unnecessary connections among the functions — or neurons — in order to adapt them to low-powered devices, a process that’s commonly known as pruning. (They specifically chose connections that had the lowest “weights,” which indicated that they were the least important.) Next, they trained the network without the pruned connections and reset the weights, and after pruning additional connections over time, they determined how much could be removed without affecting the model’s predictive ability.

After repeating the process tens of thousands of times on different networks in a range of conditions, they report that the AI models they identified were consistently less 10% to 20% of the size of their fully connected parent networks.

“It was surprising to see that re-setting a well-performing network would often result in something better,” says coauthor and assistant professor Michael Carbin. “This suggests that whatever we were doing the first time around wasn’t exactly optimal and that there’s room for improving how these models learn to improve themselves.”

Carbin and Frankle note that they only considered vision-centric classification tasks on smaller data sets, and they leave to future work exploring why certain subnetworks are particularly adept at learning and ways to quickly spot these subnetworks. However, they believe that the results may have implications for transfer learning, a technique where networks trained for one task are adapted to another task.

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How to read the Dynamics 365 Mailbox details file for troubleshooting

February 15, 2019 Microsoft Dynamics CRM

In this post, we are going to look at the Mailbox Details log file that can be used when troubleshooting Server Side Synchronization and App for Outlook scenarios. Thanks again to Cody Dinwiddie on our Dynamics 365 Support team for helping put this information together.

Now, if you don’t know, Server-Side Sync is processed by the Async Service, which means it can sometimes be difficult to find enough detail to troubleshoot ACT and Email synchronization issues. Mailbox alerts is always a great place to start but it can really only give you a few pieces of information, such as if a Test and Enable was successful or what errors the mailbox is running into. However, this will not show me other key information, such as when the next sync cycle is for the mailbox. In most cases, this is the information I am looking for and it is one of the most common questions I receive.

To find additional information, the mailbox record does have a way to get this data by selecting Download Mailbox Details on the ribbon of the appropriate mailbox record.

Once you click to download this .log file, open it up with a text editor like Notepad. Note: For a Dynamics 365 Online instance, all times will be in UTC.

Let’s break down the sections:

Mailbox Async Processing State

mailboxid : 9fb6600b-0965-e811-a97f-000d4a161089

(GUID of the mailbox record)

hostid: Null

(This will contain the name of the async server processing the request if mid-process)

processingstatecode : 0

(This will be 1 if async is currently processing this mailbox and you see a hostid populated)

processinglastattemptedon : 12/3/2018 3:23:36 PM

(This is the last time the mailbox attempted to process)

Mailbox Synchronization Methods

(EmailRouter here means Server-Side Sync)

incomingemaildeliverymethod : EmailRouter

outgoingemaildeliverymethod : EmailRouter

actdeliverymethod : EmailRouter

Mailbox Enabled State

(These states are tied to the test/enable being successful from a server-side sync perspective)

enabledforincomingemail : True

enabledforoutgoingemail : True

enabledforact : True

Mailbox Idle State

(Shows how many times the mailbox was processed with no Email items or Appointments, Contacts and Tasks to synchronize)

noemailcount : 3

(This means 3 sync cycles ago, an email was promoted from the Exchange mailbox into Dynamics)

noactcount : 2

(This means 2 sync cycles ago, an Appointment, Contact or Task was promoted from the Exchange mailbox into Dynamics)

Mailbox Backoff Parameters

(Shows when the mailbox is scheduled to process next. These are the fields I use the most)

postponemailboxprocessinguntil : 12/3/2018 3:28:37 PM

(This value controls when the Asynchronous Processing Service will run on this mailbox, which actually performs a synchronization)

postponesendinguntil : 11/29/2018 6:07:17 PM

(If this value is the same or before the postponemailboxprocessinguntil value, asynchronous emails will be sent from Dynamics on the next sync)

receivingpostponeduntil : 12/3/2018 3:28:37 PM

(If this value is the same or before the postponemailboxprocessinguntil value, emails in Exchange will attempt to sync with Dynamics)

receivingpostponeduntilforact : 12/3/2018 3:25:34 PM

(If this value is the same or before the postponemailboxprocessinguntil value, Appointments, Contacts and Tasks will attempt to sync with Dynamics)

Mailbox Test and Enable Parameters

testemailconfigurationscheduled : False

(This will be true if test/enable is pending)

testemailconfigurationretrycount : 0

(This enumerates how many times the test/enable process has attempted to retry in the event of a failure)

testmailboxaccesscompletedon : 11/29/2018 6:07:22 PM

(This provides the time the last test/enable was run on this mailbox)

postponetestemailconfigurationuntil : 11/29/2018 6:12:22 PM

(If this value is the same or before the postponemailboxprocessinguntil value, test/enable will be run on the mailbox)

Mailbox Last Sync Cycle Information

lastsuccessfulsynccompletedon : 12/3/2018 3:23:37 PM

(This provides the time that the mailbox last performed a sync without running into an exception/ error)

lastsyncerror: Null

(This will provide limited exception details of what error occurred on the last mailbox sync attempt, if one occurred)

lastsyncerrorcode: Null

(This will provide the exception code for the last error, if available)

lastsyncerrorcount : 0

(This will enumerate how many consecutive times the same error has occurred for the mailbox synchronization)

lastsyncerroroccurredon : 10/17/2018 5:41:58 PM

(This provides the time the last error occurred for the mailbox. If this time is before the lastsuccessfulsynccompletedon and processinglastattemptedon times, no error happened on the last sync)

itemsprocessedforlastsync : 2

(This enumerates how many Exchange items were successfully promoted on the last sync cycle)

itemsfailedforlastsync : 0

(This enumerates how many items succeeded the promotion criteria, but failed to promote to Dynamics)

Email Server Profile Details

(This section provides details on the Email Server Profile configured to this mailbox)

Email Server Profile General Settings (servertype: 1 is others, 0 is Exchange)

servertype : 0

(0 is Exchange, 1 is others, such as POP3)

useautodiscover : True

(False indicates that the EWS URL is explicitly defined in the Email Server Profile)

maxconcurrentconnections : 108

(This value defines how many simultaneous connections to Exchange that this Email Server Profile can handle)

minpollingintervalinminutes : 0

(This value determines how often Asynchronous processing is attempted for a mailbox in minutes. The minimum value is 5, so “0” in this context means that mailboxes sync every 5 minutes)

Email Server Profile Incoming Email Settings

incomingauthenticationprotocol : AutoDetect

(This value defines what authentication method is being used, such as through an impersonation account or via mailbox credentials etc..)

incomingcredentialretrieval : S2S

(This defines the authentication credentials being used, such as S2S (server-to-server), OAuth, etc..)

incominguseimpersonation : False

(This defines if an account with Application Impersonation is being used for incoming email synchronization)

incomingusessl : True

(This defines if synchronization of incoming items uses certificates for encryption)

incomingportnumber : 443

(This defines the port being used for incoming synchronization)

Email Server Profile Outgoing Email Settings

outgoingauthenticationprotocol : AutoDetect

(This value defines what authentication method is being used, such as through an impersonation account, via mailbox credentials etc..)

outgoingcredentialretrieval : S2S

(This defines the authentication credentials being used, such as S2S (server-to-server), OAuth, etc..)

outgoinguseimpersonation : False

(This defines if an account with Application Impersonation is being used for outgoing email synchronization)

outgoingusessl : True

(This defines if synchronization of outgoing items uses certificates for encryption)

outgoingportnumber : 443

(This defines the port being used for outgoing synchronization)

Recent Trace Log details

(This section provides the last 10 mailbox specific logs for the associated mailbox. We will not be covering troubleshooting of the different errors here. Some of these will be more straightforward and some will require more telemetry review from a Microsoft resource. That is where the MailboxId and ActivityId can be used to correlate to additional logging.)

tracecode : 126

errortypedisplay : ExchangeSyncServerServiceError

errordetails : T:391

ActivityId: a2559263-94de-4886-bc28-5fc7511d4f5f

>Exception : Unhandled exception:

Exception type: System.Net.WebException

Message: The remote server returned an error: (503) Server Unavailable. at System.Net.HttpWebRequest.EndGetResponse(IAsyncResult asyncResult) at Microsoft.Exchange.WebServices.Data.EwsHttpWebRequest.Microsoft.Exchange.WebServices.Data.IEwsHttpWebRequest.EndGetResponse(IAsyncResult asyncResult) at Microsoft.Exchange.WebServices.Data.ServiceRequestBase.EndGetEwsHttpWebResponse(IEwsHttpWebRequest request, IAsyncResult asyncResult) — End stack trace — Exception type: Microsoft.Exchange.WebServices.Data.ServiceRequestException

Message: The request failed. The remote server returned an error: (503) Server Unavailable. at Microsoft.Exchange.WebServices.Data.ServiceRequestBase.EndGetEwsHttpWebResponse(IEwsHttpWebRequest request, IAsyncResult asyncResult) at Microsoft.Exchange.WebServices.Data.SimpleServiceRequestBase.EndInternalExecute(IAsyncResult asyncResult) at…

tracecode : 2

errortypedisplay : UnknownIncomingEmailIntegrationError

errordetails : ActivityId: 08657d6f-7e7b-424c-a974-6de3d4le2ae4a

>Error : ?<ResponseMessageType xmlns:q1=”http://schemas.microsoft.com/exchange/services/2006/messages” p2:type=”q1:FindItemResponseMessageType” ResponseClass=”Error” xmlns:p2=”http://www.w3.org/2001/XMLSchema-instance”><q1:MessageText>Mailbox move in progress. Try again later., Cannot open mailbox.</q1:MessageText><q1:ResponseCode>ErrorMailboxMoveInProgress</q1:ResponseCode><q1:DescriptiveLinkKey>0</q1:DescriptiveLinkKey></ResponseMessageType>

tracecode : 52

errortypedisplay : IncomingEmailServerServiceError

errordetails : ActivityId: b66413dd-c51e-43d1-9404-adb044abf655

>Error : System.Net.WebException: The request failed with HTTP status 503: Service Unavailable.

at System.Web.Services.Protocols.SoapHttpClientProtocol.ReadResponse(SoapClientMessage message, WebResponse response, Stream responseStream, Boolean asyncCall)

at System.Web.Services.Protocols.SoapHttpClientProtocol.EndInvoke(IAsyncResult asyncResult)

at Microsoft.Crm.Asynchronous.EmailConnector.ExchangeServiceBinding.EndFindItem(IAsyncResult asyncResult)

at Microsoft.Crm.Asynchronous.EmailConnector.FindItemsStep.EndCall()

at Microsoft.Crm.Asynchronous.EmailConnector.ExchangeIncomingEmailProviderStep.EndOperation()

Thanks for reading!

Aaron Richards

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Dynamics 365 Customer Engagement in the Field

Apple’s new Transparency Report page details government data requests

December 21, 2018 Big Data

In past years, Apple released bi-annual “transparency reports” to detail the personal data requests it has received from governments around the world. Today, the company has upped the ante with an upgraded Transparency Report section of its Privacy mini-site (via TechCrunch), clearly separating key request statistics for dozens of countries while offering individual report links for each of them.

At a glance, the new Transparency Report enables users to see how many requests a government has made across four categories: “Device,” “Account,” “Financial Identifier,” and “Emergency.” Device is generally used by law enforcement officials to help locate lost or stolen devices; Account seeks a user’s basic account information or content; Financial Identifier is for suspected fraudulent credit, debit, or gift card transactions; and Emergency is for situations involving imminent danger to a person.

The individual country reports go into considerable detail regarding requests, with the United States of America entry discussing everything from specific types of legal process to general notes on the nature of government inquiries. Yearly and biannual historical details are also included.

Not surprisingly, the trends over time show an increased number of requests, as well as a generally increased number of cases in which Apple has provided the data requested. As of the last reporting period, Apple provided data for 3,185 cases — 80 percent of the requested number — up from 2,182 in the comparable year-ago period. You can see the details for your own country here.

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Xiaomi’s AI restores details and enhances colors in poorly exposed photos

December 5, 2018 Big Data

We’ve all been there: You’re standing next to a smiling group of friends, ready with your smartphone to snap the perfect group selfie, but you’re forced to take a step back because your exposure settings are out of whack. Everything’s far too bright or too dim, a condition your camera’s finicky automatic settings aren’t helping any.

Researchers at Chinese smartphone giant Xiaomi describe a solution to the exposure dilemma in a new paper (“DeepExposure: Learning to Expose Photos with Asynchronously Reinforced Adversarial Learning“) accepted at NeurIPS 2018 in Montreal. In it, they describe an AI system capable of segmenting an image into multiple “sub-images,” each associated with a local exposure, that it subsequently uses to retouch the original input photo.

“The accurate exposure is the key of capturing high-quality photos in computational photography, especially for mobile phones that are limited by sizes of camera modules,” the researchers wrote. “Inspired by luminosity masks usually applied by professional photographers, in this paper, we develop a novel algorithm for learning local exposures with deep reinforcement adversarial learning.”

The AI pipeline — dubbed DeepExposure — kicks things off with image segmentation. Next comes an “action-generating” stage during which the input low-resolution, sub-images, and direct fusion of the images are concatenated and processed by a policy network that computes each’s local and global exposures. After the images pass through local and global value filters, the model completes a finishing step in which a value function evaluates the overall quality. Finally, the sub-images are blended together with the input image.

The neural networks at play here are of the generative adversarial network (GAN) variety. Broadly speaking, GANs are two-part neural networks consisting of generators that produce samples and discriminators that attempt to distinguish between the generated samples and real-world samples. To train the discriminator, the researchers randomly chose small batches of machine-retouched and expert-retouched photos; the contrast, illumination, and saturation features are extracted and concatenated with the RGB image to form an input.

They “taught” the AI system in Google’s TensorFlow framework on a Nvidia P40 Tesla GPU, and their corpus of choice was MIT-Adobe FiveK, a dataset containing 5,000 RAW photos — i.e., files with minimally processed data from the image sensor — and corresponding retouched ones edited by five experts for each photo. Specifically, they used 2,000 unretouched images, 2,000 retouched images, and 1,000 RAW images for testing.

DeepExposure outperformed state-of-the-art algorithms in key metrics, managing to consistently restore most details and styles in original images while enhancing brightness and colors.

“[Our] method bridges deep-learning methods and traditional methods of filtering: Deep-learning methods serve to learn parameters of filters, which makes more precise filtering of traditional methods,” the team wrote. “And traditional methods reduce the training time of deep-learning methods because filtering pixels is much faster than generating pixels with neural networks.”

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Distributor Details its Reasons for Choosing NetSuite over Infor, Epicor and Microsoft

November 17, 2018 NetSuite

Posted by Hayley Null, Manufacturing Industry Marketing Lead

NetSuite’s Ranga Bodla recently sat down with Tim White, CIO of L.N. Curtis & sons, a leading distributor of emergency responder equipment and previous Infor customer, to discuss the company’s decision to move off Infor for a new long-term ERP system. L.N. Curtis & sons went through a rigorous analysis and evaluation of solutions from Infor, Epicor, Microsoft and NetSuite, and ultimately chose to go with NetSuite. Below are highlights from the conversation.

Ranga Bodla: What were your key business drivers in looking for a new system?

Tim White: Our existing Infor system was inflexible and very expensive to customize. Making changes required expensive outside resources that we couldn’t develop or bring in house. Additionally, there were very few third-party applications available and no access to cloud-based applications.

We also needed to make sure that we could put in place both B2B and B2C ecommerce. Infor lacked a robust ecommerce solution and had an antiquated, undocumented API which made ecommerce integrations almost impossible. Finally, we have a mobile sales force and we needed to make sure that our sales reps were enabled with data when they were going onsite with clients.

RB: Why did L.N. Curtis & sons decide on a cloud-based system?

TW: There were a number of factors. We wanted anytime, anywhere access to the system via a browser and we knew a cloud based solution would reduce our IT costs. A true SaaS solution with regular software updates with an open architecture were very important to Curtis

RB: What were your other evaluation criteria?

TW: We needed a solution to be able to deliver a quantum leap in functionality and flexibility. For us, it didn’t make sense to select a new system and go through a new implementation for just minor improvements. It was also important that our ERP provider had a robust ecosystem of third party partners. Lastly, we wanted a solution that would fit our business, but also grow with us.

RB: What was the sales cycle like for NetSuite vs. Epicor vs. Infor vs. others?

TW: Epicor was very polished and convincing; however, once we saw it up close we realized the core technology was over 15 years old and didn’t come close to addressing our criteria. Infor mishandled the software presentation and was disqualified quickly. Microsoft Dynamics was a little bit of a different story. In our case, the partner’s presentation was unimpressive, and we disqualified them after the first round. Ultimately, we were most impressed with NetSuite – not only with the sales cycle and product demonstration, but with the strength of the platform. We were convinced the solution was right for us.

RB: How would you describe your NetSuite implementation?

TW: The implementation from NetSuite professional services and the reseller was detailed and methodical. Our business is very complex, and the teams were able to handle our more than 200 customizations. We never would’ve been able to do that many customizations on any other system. Our industry is very unique, and we consider a lot of our processes to be competitive advantage – we wanted to make sure that the system delivered on those. Nonetheless, it’s always quite a journey to implement a new ERP. Your company needs to be prepared for a lot of in-house project management and coordination to get the system live. We were lucky that we had two smaller subsidiaries that we were able to roll the ERP out to first before we rolled it out to the broader company. We used these as our “test runs” and felt completely prepared for the final roll out.

RB: Now that you’re running on NetSuite, what kinds of process improvements have you seen?

TW: Transaction user errors and order entry errors are down significantly with our enhanced control and approval processes. Data visibility and analysis improved due to NetSuite’s reporting tools and dashboards. User satisfaction increased dramatically due to a superior user interface. Finally, and one of the biggest reasons we were evaluating a new ERP, we have true ecommerce sites that are growing well and seamlessly integrated into our financials and CRM.

RB: Have any other areas of your business improved?

TW: We have complete visibility into our customer. There’s better synergy between outside sales staff, inside sales staff and management teams. We no longer need a System Administrator in the data center. Now I’m not constantly on edge about my ERP system. It’s been a great experience with NetSuite overall and we’re happy we did it.

RB: Do you have any advice for current Infor customers or customers looking to make a move from a legacy system?

TW: Your executive team must be on board, involved and fully behind the project. Ensure that you have a strong IT project manager internally to drive the evaluation. Accept that changing ERPs is a big undertaking and it will not be easy. Finally, avoid being stuck on an exact go live date. If the new ERP is not ready, then postpone until it is. We moved our go live date two or three times, but by the time we did go live, we were shipping product and taking orders with no significant issues on day one.

Download the webinar for more insight into how to select the right ERP for the long-term.

Posted on Fri, November 16, 2018
by NetSuite filed under

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Tiffany Haddish Details Brief Feud With Tracy Morgan

June 16, 2018 Humor

Tiffany Haddish isn’t bothered by what appeared to be shade by fellow comedian and The Last O.G. co-star Tracy Morgan recently.

In her cover story with The Hollywood Reporter Wednesday (June 13), the triple threat opened up about her adored career and the many lessons she’s learned along the way. One was knowing her artistic worth, which caused a bit of tiff on the set of the TBS comedy The Last O.G.

Booked before the release of Girls Trip, Haddish plays the role of Shay, Tray’s (Morgan) former girlfriend whose life has evolved during Tray’s time in prison.

As he hilariously navigates through a gentrified Brooklyn 15 years later, he also tries to win over his former flame. As her brand grew, expectations for her to bust out “She Ready-isms” as Shay came with it, which she refused to do. She also shared how her relationship with Morgan changed as well.

While doing promo for the Vulture Festival in May, Morgan took part in an interview where he brushed off a question about his co-star. “We’re not gonna go there,” he said. “Because this isn’t Tiffany’s show. This is Tracy Morgan. This is The Last O.G. If you’re going to go there, ask that about Tiffany, ask that about Cedric [The Entertainer], ask that about Kraft services, everyone that comes to work on that show. I don’t like that.”

The series was thought of years ago by Morgan and with the help of Jordan Peele, became a reality as the first season wrapped earlier this month.

As comedians like Lil Rey Howery came to Haddish’s defense on social media, the comedian shared how it didn’t bother her.

“You guys, chill. He’s probably just tired of hearing my name,” she said. “It’s exhausting. I’m tired of hearing my name. I could see how that could be irritating, like, ‘Hello, I died, people. I’m back from the dead. Tiffany’s cool, but it’s me sitting here now.’ So, I get it, I’m not mad about it, I love me some Tracy.”

During a sit down with the Associated Press in April, Morgan and Haddish were candid about their unique chemistry. “I already knew that she was a tough cookie before I got to the set,” he said. “That’s what made me pick her [for the role].”

Haddish hinted towards their artistic differences but explained how her ideas were always respected. “You’re able to give more input on how you feel about the character, what you think this should be, or that should be and your point of view of respected,” she said. “It may not be what we do, but it’s respected.”

Haddish and Morgan will reunite with the rest of the cast this summer to film season two of the comedy.

Check out more from her chat with THR here.

Source: VIBE, The Hollywood Reporter

'Struggleing' Comedy Web Series Developed by USMC Combat Veteran Headed to Emmys

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