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Language may help AI navigate new environments

April 19, 2020 Big Data

In a new study published this week on the preprint server Arxiv.org, scientists at the University of Toronto and the Vector Institute, an independent nonprofit dedicated to advancing AI, propose BabyAI++, a platform to study whether descriptive texts help AI to generalize across dynamic environments. Both it and several baseline models will soon be available on GitHub.

One of the most powerful techniques in machine learning — reinforcement learning, which entails spurring software agents toward goals via rewards — is also one of the most flawed. It’s sample inefficient, meaning it requires a large number of compute cycles to complete, and without additional data to cover variations, it adapts poorly to environments that differ from the training environment.

It’s theorized that prior knowledge of tasks through structured language could be combined with reinforcement learning to mitigate its shortcomings, and BabyAI++ was designed to put this theory to the test. To this end, the platform builds upon an existing reinforcement learning framework — BabyAI — to generate various dynamic, color tile-based environments along with texts that describe their layouts in detail.

Language may help AI navigate new environments

Above: Environments in BabyAI++.

BabyAI++’s levels consist of objects that can be picked up and dropped; doors that can be unlocked and opened; and various tasks that the agents must be undertake. Like the environments themselves, the tasks are randomly generated, and they’re communicated to the agent through “Baby-Language,” a compositional language that uses a subset of English vocabulary.

The abovementioned texts reveal which types of tiles are in use and what color is matched to each tile. Since the pairing between the color and tile type is randomized, the agent must understand the description for it to properly navigate the map.

Within BabyAI++, every level is partitioned into two configurations: training and testing. In the training configuration, the agent is exposed to all tile and colors types in the level, but some combinations of color-type pairs are held out. In the testing configuration, all color-type pairs are enabled, forcing the agent to use language grounding to associate the type of the tile to the color.

The paper describes several experiments that were conducted using the baseline models, one of which — attention-fusion — uses what’s called an attention mechanism to assign relevant text embeddings (mathematical representations) to locations on a scene embedding feature map (a function that maps embeddings to a feature space, or the dimensions where the variables the AI processes reside). For the most difficult level, this attention-fusion model had a 16.2% higher testing success rate (around 60% after 5 steps, or actions) than the second-best model on the most challenging level, and it completed the level using fewer frames of images (around 65 compared with 75).

Above: The attention model’s architecture.

The coauthors assert that this shows descriptive texts are useful for agents to generalize environments with variable dynamics by learning language-grounding. “We believe the proposed BabyAI++ platform, with its public code and baseline implementations, will further spur research development in this area,” they wrote in the paper.

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Big Data – VentureBeat

MIT CSAIL’s TextFooler generates adversarial text to strengthen natural language models

February 9, 2020 Big Data

AI and machine learning algorithms are vulnerable to adversarial samples that have alterations from the originals. That’s especially problematic as natural language models become capable of generating humanlike text, because of their attractiveness to malicious actors who would use them to produce misleading media. In pursuit of a technique that illustrates the extent to which adversarial text can affect model prediction, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), the University of Hong Kong, and Singapore’s Agency for Science, Technology, and Research developed TextFooler, a baseline framework for synthesizing adversarial text examples. They claim in a paper that it was able to successfully attack three leading target models, including Google’s BERT.

“If those tools are vulnerable to purposeful adversarial attacking, then the consequences may be disastrous,” said Di Jin, MIT Ph.D. student and lead author on the paper, who noted that the adversarial examples produced by TextFooler could improve the robustness of AI models trained on them. “These tools need to have effective defense approaches to protect themselves, and in order to make such a safe defense system, we need to first examine the adversarial methods.”

The researchers assert that besides the ability to fool AI models, the outputs of a natural language “attacking” system like TextFooler should meet certain criteria: human prediction consistency, such that human predictions remain unchanged; semantic similarity, such that crafted examples bear the same meaning as the source; and language fluency, such that generated examples look natural and grammatical. TextFooler meets all three even when no model architecture or parameters (values that influence model performance) are available — i.e., black-box scenarios.

It achieves this by identifying the most important words for the target models and replacing them with semantically similar and grammatically correct words until the prediction is altered. TextFooler is applied to two different tasks — text classification and entailment (the relationship between text fragments in a sentence) — with the goal of changing the classification or invalidating the entailment judgment of the original models. For instance, given the input “The characters, cast in impossibly contrived situations, are totally estranged from reality,” TextFooler might output “The characters, cast in impossibly engineered circumstances, are fully estranged from reality.”

To evaluate TextFooler, the researchers applied it to text classification data sets with various properties, including news topic classification, fake news detection, and sentence- and document-level sentiment analysis, where the average text length ranged from tens of words to hundreds of words. For each data set, they trained the aforementioned state-of-the-art models on a training set before generating adversarial examples semantically similar to the test set to attack those models.

The team reports that on the adversarial examples, they managed to reduce the accuracy of almost all target models in all tasks to below 10% with fewer than 20% of the original words perturbed. Even for BERT, which attained relatively robust performance compared with the other models tested, TextFooler reduced its prediction accuracy by about 5 to 7 times on a classification task and about 9 to 22 times on an entailment task (where the goal was to judge whether a sentence could be derived from entailment, contradiction, or a neutral relationship).

“The system can be used or extended to attack any classification-based NLP models to test their robustness,” said Jin. “On the other hand, the generated adversaries can be used to improve the robustness and generalization of deep learning models via adversarial training, which is a critical direction of this work.”

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Big Data – VentureBeat

Storage 101: The Language of Storage

February 2, 2020 BI News and Info

The series so far:

Storage drives come in many shapes and sizes, and it can be difficult to distinguish one from the other beyond their appearances because vendor-supplied information is sometimes confusing and obscure. Although the material has gotten better over the years, it can still be unclear. Yet understanding this information is essential to knowing how well a drive will perform, how much data it will hold, its expected lifespan, and other important features.

In the first article in this series, I introduced you to a number of storage-related concepts, all of which can play an important role in determining what each drive offers and how they differ. For example, a solid-state drive (SSD) that uses the Peripheral Component Interconnect Express (PCIe) interface will typically perform better than one that uses the Serial Advanced Technology Attachment (SATA) interface, and a SATA-based SSD will likely perform better than a SATA-based hard-disk-drive (HDD).

But the interface and drive type are only part of the equation when it comes to choosing storage media. You must also take into account latencies, input/output operations per second (IOPS), throughput, effective and usable capacities, data transfer size or I/O size, endurance, and other factors. Unfortunately, it’s no small matter trying to make sense of all these variables, especially with the inconsistencies among storage vendors in how they present information about their products.

In this article, I dig into concepts commonly used to describe storage media to help make sense of the information you’ll encounter when evaluating HDDs or SSDs for your organization. Many of the concepts are specific to performance, but I also discuss issues related to capacity and lifespan, particularly in how they differ between HDDs and SSDs.

Making Sense of Performance Metrics

When architecting storage solutions to meet enterprise requirements such as performance, you should identify the workloads that the devices must support. To this end, you must understand data access patterns such as read operations versus writes, random operations versus sequential, and block transfer size.

In this regard, storage operations can be divided into four types: random reads, random writes, sequential reads, and sequential writes. In some cases, these operations can be further divided by the block transfer size (small versus large), which depends on the application. Many workloads use a mix of two or more of these types, although they might favor one over the others. Common data access patterns include:

Random read/write, small block: A wide variety of applications such as transactional business applications and associated databases.
Sequential write, large block: Loading media, loading data warehouse.
Sequential read, large block: Reading media, data warehouse aggregations and reporting.
Sequential write, small block: Database log writes.
Mixed: Any combination of the above. Note that, when multiple sequential workloads are active concurrently, the workload becomes randomized.

When shopping for storage, you’ll encounter an assortment of metrics that describe how well a drive is expected to perform. Understanding these metrics is essential to ensuring that you’re using the right drives to support your specific workloads.

Latency refers to a drive’s response time, that is, how long it takes for an I/O operation to complete. From an application’s perspective, latency is the time between issuing a request and receiving a response. From a user perspective, latency is the only metric that matters.

Vendors list latency in milliseconds (ms) or microseconds (µs). The lower the number, the shorter the wait times. However, the rate can quickly increase as individual I/O requests start piling up, the I/O sizes increase (which typically range between 4 KB to 512 KB), or the nature of the workload changes, such as changing from read-only to read/write or from sequential to random. For example, a drive’s latency might be listed as 20ms, but if the drive is supporting concurrent read operations, I/O requests could end up in a long queue, causing a dramatic increase in latency.

Latency is nearly always a useful metric when shopping for drives and should be considered in conjunction with IOPS. For example, a storage solution that provides 185 IOPS with an average latency of 5ms might deliver better application performance than a drive that offers 440 IOPS but with 30ms latency. It all depends on the workloads that the drives will need to support.

Another common metric is IOPS, which indicates the maximum number of I/O operations per second that the drive is expected to support. An I/O operation is the transfer of data to or from the drive. The higher the number of supported IOPS, the better the performance—at least according to conventional wisdom. In truth, IOPS tells only part of the story and should be considered along with other important factors, such as latency and I/O size. IOPs is most relevant for random data access patterns (and far less important for sequential workloads).

Another important metric is throughput, which measures the amount of data that can be written to or read from a storage drive within a given timeframe. Some resources may refer instead to data transfer rate or simply transfer rate, sometimes according to drive type. For example, you might see transfer rate used more often with HDDs and throughput associated with SSDs. Like other performance metrics, throughput is dictated by the nature of the workload. Throughput is most relevant for sequential data access patterns (and far less relevant for random workloads).

The distinction between sequential and random is particularly important for HDDs because of how data is stored on the platters, although it can also play a role in SSD performance. In fact, there are other differences between the two drive types that you need to understand when evaluating storage. Discussions of device capacity and endurance also follow.

What Sets HDDs Apart

Data is written to an HDD in blocks that are stored sequentially or scattered randomly across a platter. Enterprise drives contain multiple platters with coordinated actuator arms and read/write heads that move across the platters (a topic I’ll be covering more in-depth later in the series).

Whenever an application tries to access the data, the platter’s actuator arm must move the head to the correct track and the platter must be rotated to the correct sector. The time required to do so is referred to as the seek time. The time it takes for the platter to rotate to the correct sector is referred to as the rotational latency.

The duration of an I/O operation depends on the location of the head and platter prior to the request. When the data blocks are saved sequentially on the disk, an application can read and write data in relatively short order, reducing seek times and rotational latencies to practically nothing. If the blocks are strewn randomly across the platters, every operation requires the actuator to move the head to a different area on the platter, resulting in rotational latency and seek time, and therefore slower performance.

Because of these differences, it is essential to evaluate HDDs in terms of the workloads you plan to support, taking into account such factors as file size, concurrency, and data access patterns (random vs. sequential, read vs. write, big block vs. small block, and mixed). By taking your workloads into account, you’ll have a better sense of how to select the right drives for the workloads demanded by your organization’s applications.

For example, Dell offers a 12-TB SATA drive that supports up to 118 IOPS for random reads and 148 IOPS for random operations that include 70% reads and 30% writes at a given latency. Whereas the same drive offers a throughput of 228 MB/s for sequential read and write operations. From these metrics, you can start getting a sense of whether the drive can meet the needs of your anticipated workloads.

Suppose you’re looking for a storage solution to support a set of read-intensive web applications whose storage access pattern is primarily random reads, as opposed to sequential reads. You would want to compare the Dell drive against other drives to determine which one might offer the best IOPS, with less emphasis placed on the other types of access patterns.

One characteristic driving HDD performance is revolutions per minute (RPM), that is, the number of times the drive’s platters rotates within a minute. The higher the number of RPMs, the faster the data can be accessed, leading to lower latency rates and higher performance. Enterprise-class HDDs typically support 10,000 or 15,000 RPMs, often written as simply 10K or 15K RPMs.

You must also take into account a drive’s available capacity, keeping in mind that you never load an HDD anything close to its physical capacity.

A drive’s anticipated lifespan is indicated by the mean time between failures (MTBF) rating, the number of operating hours expected before failure. For example, Dell offers several 14-TB and 16-TB drives with MTBF ratings of 2,500,000 hours, which comes to over 285 years. Such ratings are common, yet in reality drives fail far more frequently than high MTBF suggests. MTBF is only a small part of the reliability equation. Enterprise solutions demand the identification and elimination of single points of failure and redundancies across components, starting at the drive level.

By looking at the various metrics, you have a foundation to begin comparing drives. Historically, marketing considerations—trying to present their drives in the best light—resulted in presenting performance specs in a way that made comparisons across vendors challenging. Today’s specifications are generally more consistent, at least enough to make reasonable apples-to-apples comparisons. Some vendors also provide insights beyond the basics, for example, providing performance metrics featuring a mix of workloads, such as 70% reads and 30% writes at a given latency.

What Sets SSDs Apart

Consumer and enterprise SSDs are based on NAND flash technologies, a type of nonvolatile memory in which data is stored by programming integrated circuit chips, rather than manipulating magnetic properties, as with an HDD. Also unlike the HDD, the SSD has no moving parts, which makes reading and writing data much faster operations.

If you’re relatively new to enterprise SSDs, the terminology that surrounds these drives can be confusing. Yet the fundamental performance considerations are exactly the same: latency, IOPs, throughput, capacity, and endurance .

As with an HDD, capacity in an SSD refers to the amount of data it can hold. With SSDs, however, vendors often toss around multiple terms related to capacity and do so inconsistently, so it’s not always clear what each one means. For example, some vendors list a drive’s total capacity as raw capacity or just capacity, both of which refer to the same thing—the maximum amount of raw data that the drive can hold.

Not all of the drive’s raw capacity is available for storing data. The drive must be able to accommodate the system overhead required to support various internal SSD operations. For this reason, the amount of available capacity is always less than the amount of raw capacity. Vendors often refer to available capacity as usable capacity.

Conceptually, raw capacity and usable capacity are similar between HDDs and SSDs. For example, when calculating capacities, you should take into account that some of that space must be available for system data, as well as for recovery configurations such as RAID.

Another characteristic that sets the SSD apart is the way in which bits are stored in the flash memory cells. Today’s SSDs can store up to four bits per cell, with talk of five bits in the wings. Vendors often reference the bit-level in the drive’s specifications. For example, a drive that supports three bits per cell is referred to as triple-level cell (TLC) flash, and a drive that supports four bits per cell is referred to as quad-level cell (QLC) flash.

In addition to squeezing more bits into a cell, vendors are also trying to get more cells onto a NAND chip. The more bits the chip can support, the greater the data density.

As with HDDs, SSD endurance refers to the drive’s longevity or lifespan; however, it’s measured differently. SSD drive endurance is based on the number of program/erase cycles (P/E cycles) it will support. NAND cells can tolerate only a limited number of P/E cycles. The higher that number, the greater the endurance. A drive’s endurance is measured by its write operations because read operations have minimal impact on an SSD. Whereas the HDD endurance metric is MTBF, vendors report an SSD’s endurance by providing the number of drive writes per day (DWPD), terabytes written (TBW), or both. The DWPD metric refers to the number of times you can completely overwrite the drive’s capacity each day during its warranted lifespan. The TBW metric indicates the total number of write operations that the drive will support over its lifetime. The DWPD and TBW are quite useful for comparing drives.

As vendors squeeze more bits into each cell and more cells into each chip, SSD vendors incorporate sophisticated technologies to mitigate the challenges of maintaining data integrity concomitant with higher data densities. For example, all SSDs employ wear leveling, over-provisioning, garbage collection, and error correction code (ECC) to extend the drive’s life, all of which I’ll be covering in more detail later in the series.

Moving toward a better understanding of storage

You should use the vendors’ published information only as a starting point for evaluating drives and identifying candidates. Consider additional research such as community reviews, benchmarks, or other resources to give you a realistic understanding of a drive’s capabilities. Your goal is to get as complete a picture as possible before investing in and testing solutions.

In addition, when comparing storage solutions, be sure to take into account the effective capacity, which is the amount of storage available to a drive after applying data-reduction technologies such as deduplication and compression. Such data-reduction strategies make it possible for the drive to store much more data. For example, IBM offers a flash-based storage system that provides 36.9 TB of raw capacity but supports up to 110 TB of effective capacity

Clearly, you need to understand a wide range of concepts to determine what types of storage will best support your workloads. Not only must you choose between HDDs and SSDs, but you must also select from within these categories, while taking into account such factors as latency, IOPs, throughput, densities, and numerous others considerations.

In the articles to follow, I’ll be digging into HDDs and SSDs more deeply so you can better understand their architectures and how they differ in terms of capacity, performance, and endurance. With this foundation, you’ll be better equipped to determine what storage solutions you might need for your organization, based on your applications and workloads.

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2020 Data Trends: Data Becomes The New Language For Innovation

February 1, 2020 BI News and Info

Have you heard about the Riddell Speedflex Precision Diamond Helmets that feature a 3D-printed liner customized for each athlete? This innovative production approach is aimed at making football safer by giving players extra protection to prevent concussions. Riddell is leveraging data to make these enhancements possible.

Data to define the future

The best way to predict the future is to create it. Riddell tapped the wealth in data to create a safer future for players. In a true sense, innovations are our way of creating the future.

Thanks to the ever-growing data leading the way, the most unusual creations of our times have emerged. Data-driven innovations now form a key growth pillar for most organizations that create significant competitive advantages.

Data-driven outcomes to foster new industries, processes, and products

Collect data. Zero down on the precise data set you need, and render it into products and solutions that perform better. Data sets are becoming a core asset in the economy. Continually emerging data-driven insights showcase how far we’ve come and challenge us to create new things.

Data will continue to add value to multiple aspects of human life and business parameters. With cognitive systems becoming more and more efficient, workers and organizations will be impacted by the power of more information, and text, speech, and images will provide more data sources for broadening analytics projects. Image recognition, text recognition, and face recognition will continue to be significant inputs for analytics applications.

Data-driven innovation hubs and startups

Businesses will create data-driven innovation hubs housing expertise and facilities to help industrial sectors become more innovative through data. The research will create spin-outs, attracting startups and established businesses and driving public- and private-sector investment.

Fintechs will be the winners in innovation hubs as they focus on niche innovations in fraud management, creating new products and simplifying processes. Utilities will witness a reboot.

Customer experience management

A data-driven company is only as good as the insights it gathers. Creativity will drive artificial intelligence and machine learning applications as we optimize the systems and platforms, and the confluence of data from various sources will enable the creation of more competent, reliable, and dependable products.

Collaboration will become more common as new niche entrants cooperate with tech giants that are sitting on a wealth of user data. 2020 will witness changes in platforms through data, and many new platforms will be born from insights within data sets.

Social innovations

2020 will be a time when citizens will benefit from the data revolution as it helps economic growth, creates social change, and optimizes public services. With enough data available, governments will increase the use of data-driven innovations for sustainability and disaster-management initiatives.

Frugality in data capture and retention

With IoT and 5G leading the way, data will grow in spread, scope, and scale in 2020, increasing the problem of too much data. To add to the problem, a lot of data has a limited lifespan. At some point, it loses relevance and becomes inexact or outdated. But often, it is held in the mistaken belief that someday it might become useful. The adage “garbage in/garbage out” will be more valid than ever.

Organizations will start evaluating data strategies and reexamine the vast stores of data they have no idea what to do with and no hope of learning anything useful from. Unless they put good data governance in place, the organizations already struggling to store and analyze their data will drown in it.

Organizations will come to terms with the fact that collecting and storing data costs money – data requires storage, electricity to power it, and, if the information is sensitive (e.g., customer records), attention must be paid to security and data compliance. So they will become frugal in their pursuit of data.

See what else is on analysts’ radar in “Deloitte Report: Tech Trends 2020.”

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Digitalist Magazine

Amazon explores a way to preserve privacy in natural language processing

January 23, 2020 Big Data

Can privacy and security be preserved in the course of large-scale textual data analysis? As it turns out, yes. A team of Amazon researchers in a recently published study proposed a way to anonymize customer-supplied data. They claim that their approach, which works by rephrasing samples and basing the analysis on the new phrasing, results in at least 20-fold greater guarantees on expected privacy.

“Questions about data privacy are frequently met with the answer ‘It’s anonymized! Identifying features have been scrubbed!’ However, studies … show that attackers can deanonymize data by correlating it with ‘side information’ from other data sources,” Tom Diethe, machine learning manager in the Amazon Alexa Shopping organization, wrote in a blog post.

The researchers’ differential privacy solution involved adding noise to make data related to specific people more difficult to trace. (This noise resulted in a loss of accuracy, but the expectation is that as the size of the data set grows, the trade-off between usefulness and privacy will become more manageable.) As Diethe notes, differential privacy provides a statistical assurance that aggregate data won’t leak information about which individuals are in the data set. Given the result of an analysis, the probabilities that either of the two data sets was the basis of the analysis should be virtually identical.

The difference between probabilities is controlled by a parameter — epsilon — that must be determined in advance. With metric differential privacy — the type of differential privacy applied by the Amazon team — this parameter is equal to epsilon multiplied by the distance between the two data sets according to some metric, such that the data sets are increasingly difficult to distinguish the more similar they become.

The study’s coauthors analyzed the privacy implications of different choices of epsilon value in the context of natural language processing, where word embeddings — mappings from the space of words into a vector (i.e., the space of real numbers) — often depend on the frequency with which words co-occur. This presents a challenge where differential privacy is concerned, because adding noise in the space of a word embedding usually produces a new point in the representational space that’s not the location of a valid word embedding. A search must be performed to find the nearest valid embedding once such a point is identified.

The team attempted to predict, given an epsilon value, the likelihood a word in a string of words will be overwritten by noise and the number of semantically related words that will fall within a fixed distance of words in the embedding space. The aforementioned space is a hyperbolic space, where the distance between embeddings indicates semantic similarity and where the different curvature at different locations indicates where embeddings fall in a semantic hierarchy. (For instance, the embeddings of the words “ibuprofen,” “medication,” and “drug” might lie near each other in the space, but their positions along the curve indicate which are more specific terms and which more general.)

According to the researchers, this technique ensures more general terms are substituted for more specific ones and thus makes personal data harder to extract. They intend to expound upon their findings at the ACM Web Search and Data Mining (WSDM) Conference next month in Houston, Texas.

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Big Data – VentureBeat

Microsoft’s Andrew Shuman on the Cortana app’s death, natural language, and Alexa

December 23, 2019 Big Data

Last month, news broke that Microsoft was killing off Cortana for Android and iOS and was removing Cortana from its Launcher app for Android. On January 31, 2020, Microsoft will end support for the Cortana app in Australia, Canada, China, Germany, India, Mexico, Spain, and the U.K. This was Microsoft’s statement at the time:

Cortana is an integral part of our broader vision to bring the power of conversational computing and productivity to all our platforms and devices. To make Cortana as helpful as possible, we’re integrating Cortana deeper into your Microsoft 365 productivity apps, and part of this evolution involves ending support for the Cortana mobile app on Android and iOS.

Microsoft refocusing Cortana for the enterprise, and specifically for Windows and Office, is not new. Nonetheless, killing off the Cortana app was a bold move. We sat down with Andrew Shuman, who has been leading the Cortana team since Javier Soltero’s departure last year, to find out why Cortana for Android and iOS is being killed off, what to expect with Cortana for Windows, his thoughts on natural language and typing, what’s going on with the Alexa integration, and more.

Cortana for Android and iOS in the U.S.

Shuman confirmed that the Cortana mobile app (and Cortana integration in Microsoft’s Android launcher) was going away in all countries except for the U.S. Previously, Microsoft listed only eight countries that would lose Cortana for Android and iOS. But other countries, such as Japan, will also be losing it. At this point, why not kill the Cortana app in the U.S. as well?

Shuman gave a few reasons. First, he said, the Cortana app is how you configure Cortana and update the firmware on Surface Headphones, which debuted only in the U.S. “We still have people who use their Cortana apps to manage their headphone operation,” said Shuman. “So if you have the Surface Headphones, you can use the Cortana app with those, and we’re still supporting that.”

That’s not a great reason. After all, Microsoft started selling Surface Headphones in Australia, Austria, Canada, France, Germany, Ireland, New Zealand, and Switzerland earlier this year. After January 30, only Surface Headphones users in the U.S. will be able to keep using Cortana for Android and iOS. All other Surface Headphones owners will have to use Cortana for Windows.

We did get some good news for these Surface Headphones owners. In the spring, Microsoft plans to launch the delayed Surface Earbuds. The accompanying Surface Audio app will not only be available for Android, iOS, and Windows, it will support existing Surface Headphones and the new Surface Earbuds for managing all settings and configuring Cortana.

Next, Shuman surprisingly mentioned the only Cortana smart speaker that saw the light of day — a device Microsoft executives typically avoid discussing. “Also, people who have Invokes in market can still use the Cortana app to configure their Invokes, so those markets are all still going,” said Shuman.

This explanation does have some legs — the Harmon Kardon Invoke was only ever sold in the U.S. But that’s because sales were so low — Microsoft was selling it at 50% off just a month after launch.

Shuman gave two more reasons for why the Cortana app is being killed in all countries except the U.S. First, Cortana for Android and iOS didn’t receive much traction outside the U.S. “The markets we turned off had much lower usage and engagement,” he said.

And finally, Cortana for Android and iOS is sticking around in the U.S. because Microsoft wants to keep experimenting. “We also think that there may be some roles for standalone assistants as an experimentation place for us to try out new ideas still. I think this point has been made a lot, but … the assistant landscape is rich with opportunity, and very not rich with actual results, sometimes. And so the opportunity to continue to try things quickly is important to us as well.”

Cortana for Windows, Office, and beyond

The latest Cortana for Windows improvements are slated for the next major Windows 10 update, likely to arrive in April or May of next year. If Cortana for Android and iOS didn’t do well in some markets outside the U.S., how is it doing on Windows?

“Cortana on Windows does really well in those eight markets, and that’s part of the reason we had started to bring the mobile app there,” said Shuman. “Given our focus on the productivity users we think that … it makes much more sense to be where our users are inside of Office and inside of Windows.”

In other words, rival assistants have won on the phone. So Microsoft is focusing on the enterprise.

Where else can we expect Cortana to show up? Shuman noted that it’s coming to Microsoft Teams next year, but there’s no release date for that yet.

“Obviously, there’s strength in enterprises, but the kind of problems we have are very universal,” he continued. “It’s about helping people get time back. The previous apps that we were showing before were really great when we were in startup mode and trying to sort out what an assistant could be. But they were not directly aligning with those Microsoft 365 users who really don’t want to go to another experience to do their stuff. They want to be able to do it in the apps they’re using every day, like Outlook and like Teams. That’s really been our push now — over a year now of work that we’ve had underway.”

Windows and Office aside, you should expect Cortana in every Microsoft app Microsoft can think of.

“The analogy that I like to use is both Microsoft Search or Microsoft Account, which are really across all of our applications,” said Shuman. “We think of Cortana in a very similar way, where it really is a horizontal surface area that’s very person-centric. It very much knows a lot about me, and then it experiences itself across all of these apps and suites. So that’s the way that we characterize the investment moving forward. And partially, that’s just the reality of wanting to be in the experiences you’re using every day and not [having] to switch over to another one to get the experience.”

Natural language and typing

Currently, you can talk, but not type, to Cortana. Not everyone wants to talk to their phone or computer, so some assistants let you type instead. Shuman confirmed that’s in the works for Cortana, too.

“I’m really glad you’re asking that, because I think there is [an] equivalence that people do with voice and assistants, when I think it really should be broadened to be about natural language and assistants,” said Shuman. “There are two or three places where I think this is really powerful. In the Play My Emails experience, for example, we had to do a lot of work on natural language understanding just to help read out emails to you intelligently. You can imagine if you just flipped open an Outlook pane and started to read everything on the screen how awful that would be.”

“There are all these labels and all these things that your brain doesn’t read when you’re scanning it,” he continued. “You just scan through the subjects. And then you scan through who sent it. And we had to do a lot of natural language processing work so that we can read them intelligently and summarize the state of affairs.”

Unlike screen readers, which simply read out every single piece of text on the screen, Outlook’s voice mode and the Play My Emails feature can give you a summary of your inbox. It can say things like, “John just replied and added you to a conversation about this week’s budget.” Cortana in Outlook skips the header between email replies, as well as the signature file at the bottom, links, attachments, and so on.

Shuman confirmed we can expect being able to type to Cortana next year.

“One of the things that we’re investing in Windows is this idea of being a quicker experience for people who are good typists. Those of us who’ve been around computers for a while don’t need to talk to them. But still, the power of natural language is really great. I’ll give you one example that I use now all the time on the builds I’m running, which is just managing my time in my calendar. It’s a lot easier to type in ‘dentist appointment next Friday’ than to alt-tab to Outlook, new file, tab, tab, tab, type in ‘dentist,’ type in ‘Friday,’ and find a free time. So that’s a great example, where just getting data into and out of my digital calendar is really simplified by natural language understanding.”

Alexa and privacy

We haven’t heard much about the Alexa-Cortana tie-up since the assistants crossed over to Echo speakers and Windows 10 PCs in August 2018, but Shuman talked about some of the companies’ future plans.

“We still have a great aspiration to do a lot more … kind of across the two companies,” he said. “It started really tops down. I don’t know if you remember that, but Satya and Jeff actually cooked this plan up. You can still invoke Cortana from Alexa, and vice versa. I think it will be a great area for us to lean into. We really believe in a multi-assistant world. Just like in the real world where I might have a doctor, and a lawyer, maybe a trainer, you’re going to have multiple assistants that are good at what they’re good at. We’re not going to become an e-commerce company anytime soon, but assistants are great at helping you buy things.”

(Alexa isn’t a great shopping assistant, but that’s another story.)

One would think that the Alexa-Cortana crossover seems like it could be in jeopardy, but Shuman said all is well. “We are still invested in it, for sure,” said Shuman. He was also positive when questioned whether anyone actually used it. “Yeah, we have a reasonable amount of usage. It’s still a very new experience but, it’s okay.”

Part of the problem is that it’s a pain to set up the two assistants. You need Alexa set up with an Amazon account, Cortana set up with a Microsoft account, and then you have to link them together. “We certainly would like to make it easier and easier to connect the two accounts, but we also want to be very clear to the user about when they’re using personalized data in an Amazon service or in a Microsoft service,” said Shuman.

Where your data goes

Logging in is necessary so each company can disclose what the assistant has collected from you. Conversely, there’s little reason you would want to launch the other assistant without being logged in. Any general question you may want to ask the second assistant, you can presumably just ask the first.

Shuman says Microsoft is becoming more conscious of users’ data “especially as we move out of the more pure consumer space into the enterprise and office productivity space.” (In August, Microsoft updated its privacy policy to say employees or contractors may listen to recordings from Cortana and Skype Translator, after a Vice report revealed the practice.)

“That definitely brings its own challenges and also its own opportunities,” said Shuman. “We do complete eyes-off AI models, which means that no one in my team or in any of the teams at Microsoft looks at your email. We do it through sophisticated sampling of public datasets and employee-donated data, and then we generalize it. Which is a huge amount of work and a huge technical breakthrough for us to really be truly eyes-off in how we build these models.”

“If you are using Office 365 services, we have a very strict compliant data storage model that ensures that no one can look at your data, except you. As we want to bring that to other experiences and other platforms, we have to be very cognizant of that promise,” he added.

Good assistants have to know you

Microsoft is still in the process of pivoting Cortana to be a productivity assistant. This productivity focus killed the mobile app, except in the U.S. where it still has traction and can be used for experimentation. The productivity angle also explains work on natural language, typing, and knowing its users.

“For us to be a really good digital assistant, we have to know you and know you well,” Shuman said. “If you go after a user like an Office user, those are the people that we can help the most. We understand the projects they’re working on. We understand the people they communicate with the most.”

Shuman launched into an example: Say you tell Cortana to “call Jon.” There are many Jons at a company like Microsoft, but, he said, “Anyone who knows me knows there’s one Jon that I work with all the time — Jon Hamaker, he’s right down the hall. And so in my experience, I want to ‘call Jon’, to be Jon Hamaker.”

“That’s the kind of thing that’s truly important about an assistant. An assistant has to really know you. And that’s absolutely core to why we’re having this renewed focus on these users that we think we can offer the most value to.”

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Google launches AutoML Natural Language with improved text classification and model training

December 13, 2019 Big Data

Earlier this year, Google took the wraps off of AutoML Natural Language, an extension of its Cloud AutoML machine learning platform to the natural language processing domain. After a months-long beta, AutoML today launched in general availability for customers globally, with support for tasks like classification, sentiment analysis, and entity extraction, as well as a range of file formats, including native and scanned PDFs.

By way of refresher, AutoML Natural Language taps machine learning to reveal the structure and meaning of text from emails, chat logs, social media posts, and more. It can extract information about people, places, and events both from uploaded and pasted text or Google Cloud Storage documents, and it allows users to train their own custom AI models to classify, detect, and analyze things like sentiment, entities, content, and syntax. It furthermore offers custom entity extraction, which enables the identification of domain-specific entities within documents that don’t appear in standard language models.

AutoML Natural Language has over 5,000 classification labels and allows training on up to 1 million documents up to 10MB in size, which Google says makes it an excellent fit for “complex” use cases like comprehending legal files or document segmentation for organizations with large content taxonomies. It has been improved in the months since its reveal, specifically in the areas of text and document entity extraction — Google says that AutoML Natural Language now considers additional context (such as the spatial structure and layout information of a document) for model training and prediction to improve the recognition of text in invoices, receipts, resumes, and contracts.

Additionally, Google says that AutoML Natural Language is now FedRAMP-authorized at the Moderate level, meaning it has been vetted according to U.S. government specifications for data where the impact of loss is limited or serious. It says that this — along with newly introduced functionality that lets customers create a data set, train a model, and make predictions while keeping the data and related machine learning processing within a single server region — makes it easier for federal agencies to take advantage.

Already, Hearst is using AutoML Natural Language to help organize content across its domestic and international magazines, and Japanese publisher Nikkei Group is leveraging AutoML Translate to publish articles in different languages. Chicory, a third early adopter, tapped it to develop custom digital shopping and marketing solutions for grocery retailers like Kroger, Amazon, and Instacart.

The ultimate goal is to provide organizations, researchers, and businesses who require custom machine learning models a simple, no-frills way to train them, explained product manager for natural language Lewis Liu in a blog post. “Natural language processing is a valuable tool used to reveal the structure and meaning of text,” he said. “We’re continuously improving the quality of our models in partnership with Google AI research through better fine-tuning techniques, and larger model search spaces. We’re also introducing more advanced features to help AutoML Natural Language understand documents better.”

Notably, the launch of AutoML follows on the heels of AWS Textract, Amazon’s machine learning service for text and data extraction, which debuted in May. Microsoft offers a comparable service in Azure Text Analytics.

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ProBeat: Wolfram’s natural language understanding looks incredibly useful

September 15, 2019 Big Data

Wolfram Research yesterday launched Wolfram Alpha Notebook Edition for Windows, Mac, and Linux. The news largely flew under the radar, which is frankly a shame. The new tool combines Wolfram Alpha and Mathematica to give students (and teachers) a new way to build through whole computations. But it’s the natural language understanding (NLU) examples that really caught my eye.

Wolfram Research is best known for creating the modern technical computing system Mathematica and the computational knowledge engine Wolfram Alpha (stylized Wolfram|Alpha). While Wolfram Alpha is already popular with students, it’s a one-shot process: math question in, computational answers out. Wolfram Alpha Notebook Edition is supposed to keep you engaged by being more than just a powerful calculator. The only real barrier here is that it’s not free.

Natural Language Understanding in the classroom

Founder Stephen Wolfram believes Wolfram Alpha Notebook Edition will “streamline coursework, deepen understanding, enable new concepts to be taught, and effectively provide a course-based personal AI tutor for every student.” Wolfram gives some examples to drive this point home.

Here is a simple one of Wolfram Alpha Notebook Edition understanding what “that” and “it” refer to:

Here is a slightly more complicated NLU example:

And here is Wolfram Alpha Notebook Edition doing multistep computations that refer to previous results:

Even defining functions is easier with NLU:

And finally, here is Wolfram Alpha Notebook Edition correctly figuring out what “it” refers to in “integrate it again” as well as differentiating “plot it” (plot the latest result) from just “plot” (let me plot something new):

None of this is new per se, but when put in the classroom context, it can be incredibly powerful. Teachers can even assign work that students can fill out, manipulate, and save for later, all using natural language.

I wish I had Wolfram Alpha Notebook Edition as a student in university.

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Facebook founds AI Language Research Consortium to solve challenges in natural language processing

August 29, 2019 Big Data

Roughly three months ago, Facebook launched calls for research proposals in three subfields of natural language processing (NLP), the cross-disciplinary study of linguistics and AI concerned with computer-language interactions. It specifically sought “robust” deep learning approaches for NLP and computationally efficient NLP in addition to neural machine translation for low-resource dialects, ultimately in the pursuit of advancing cutting-edge research in machine translation.

That was just the start, it would seem. In a blog post today announcing 11 winning proposals among the 115 submitted from 35 countries, Facebook announced the AI Language Research Consortium, a community of partners it says will “work together to advance priority research areas” in NLP.

Details were tough to come by at press time, but Facebook says the newly formed group will foster collaboration to tackle challenging tasks like representation learning, content understanding, dialog systems, information extraction, sentiment analysis, summarization, data collection and cleaning, and speech translation. In addition to collaborating with Facebook researchers on multiyear projects and publications, members of the AI Language Research Consortium will receive funding for their research, participate in an annual research workshop, and receive access to auxiliary events at “major” NLP conferences.

“These research awards in NLP and machine translation were launched as a continuation of our long-term goal of supporting open research within the NLP community and strengthening collaboration between Facebook and academia,” wrote Facebook in a blog post. “Facebook believes strongly in open science, and we hope the consortium, as well as these research awards in NLP and machine translation, will help accelerate research in the NLP community.”

Facebook’s AI Language Research Consortium might be perceived as an answer to Amazon’s Alexa Fund, which provides up to $ 200 million in venture capital funding to fuel voice technology innovation. All participants in the Alexa Fund are afforded access to tools such as software development kit previews, plus hands-on development support for hardware or software and placement at Amazon showcase events. Separately, the Alexa Fund Fellowship supports undergraduate- and graduate-level researchers working in fields like text-to-speech, conversational artificial intelligence, automatic speech recognition, and natural language understanding.

But the consortium’s mission appears to be more platform-agnostic than Amazon’s efforts — at least on its face. In that respect, it’s somewhat akin to “AI for good” programs like the grant portion of Google’s AI for Social Good, which provides funding to researchers applying at to solve some of the world’s toughest challenges, and Microsoft’s $ 125 million initiative targeting AI applications in ecology, accessibility, humanitarian action, and cultural heritage.

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AI researchers launch SuperGLUE, a rigorous benchmark for language understanding

August 15, 2019 Big Data

Facebook AI Research, together with Google’s DeepMind, University of Washington, and New York University, today introduced SuperGLUE, a series of benchmark tasks to measure the performance of modern, high performance language-understanding AI.

SuperGLUE was made on the premise that deep learning models for conversational AI have “hit a ceiling” and need greater challenges. It uses Google’s BERT as a model performance baseline. Considered state of the art in many regards in 2018, BERT’s performance has been surpassed by a number of models this year such as Microsoft’s MT-DNN, Google’s XLNet, and Facebook’s RoBERTa, all of which were are based in part on BERT and achieve performance above a human baseline average.

SuperGLUE is preceded by the General Language Understanding Evaluation (GLUE) benchmark for language understanding in April 2018 by researchers from NYU, University of Washington, and DeepMind. SuperGLUE is designed to be more complicated than GLUE tasks, and to encourage the building of models capable of grasping more complex or nuanced language.

GLUE assigns a model a numerical score based on performance on nine English sentence understanding tasks for NLU systems, such as the Stanford Sentiment Treebank (SST-2) for deriving sentiment from a data set of online movie reviews. RoBERTa currently ranks first on GLUE’s numerical score leaderboard with state-of-the-art performance on 4 of 9 GLUE tasks.

“SuperGLUE comprises new ways to test creative approaches on a range of difficult NLP tasks focused on innovations in a number of core areas of machine learning, including sample-efficient, transfer, multitask, and self-supervised learning. To challenge researchers, we selected tasks that have varied formats, have more nuanced questions, have yet to be solved using state-of-the-art methods, and are easily solvable by people,” Facebook AI researchers said in a blog post today.

The new benchmark includes eight tasks to test a system’s ability to follow reason, recognize cause and effect, or answer yes or no questions after reading a short passage. SuperGLUE also contains Winogender, a gender bias detection tool. A SuperGLUE leaderboard will be posted online at super.gluebenchmark.com. Details about SuperGLUE can be read in a paper published on arXiv in May and revised in July.

“Current question answering systems are focused on trivia-type questions, such as whether jellyfish have a brain. This new challenge goes further by requiring machines to elaborate with in-depth answers to open-ended questions, such as ‘How do jellyfish function without a brain?’” the post reads.

To help researchers create robust language-understanding AI, NYU also released an updated version of Jiant today, a general purpose text understanding toolkit. Built on PyTorch, Jiant comes configured to work with HuggingFace PyTorch implementations of BERT and OpenAI’s GPT as well as GLUE and SuperGLUE benchmarks. Jiant is maintained by the NYU Machine Learning for Language Lab.

In other recent NLP news, on Tuesday Nvidia shared that its GPUs achieved the fastest training and inference times for BERT, and trained the largest Transformer-based NLP ever made up of 8.3 billion parameters.

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