Voice Assistants Going Embedded

January 13, 2020

Everyone knows that the use of voice assistants is exploding. Just returning from CES, it was amazing to see the number of booths that support Amazon’s Alexa and Google’s Assistant. Nevertheless, recent research from Edison-NPR is showing that US household adoption of voice assistant applications is slowing down.

Read the full blog at Embedded Computing.

Can Your Assistant Deliver?

December 9, 2019

In the old days of corporate America an “assistant” would bring you coffee. Today the “assistant” is Alexa or Google or some other voice assistant, and they don’t directly bring you coffee…yet.

FROM MUSIC TO FOOD ORDERING
Over 30% of U.S. adults now have at least one smart speaker, 60% have used a voice assistant on a smartphone, and the market penetration continues to grow. Currently people use their voice assistants for a lot of music playing, alarm setting and general queries. Another trend is occurring in parallel to the growth of voice assistants, and that is the market for ordering ahead for food to go.

Walmart already allows orders through Google Assistant, and Alexa Skills have been built for ordering from dozens if not hundreds of restaurants. A new survey from The MDR Group and Progressive Business Insights shows more than two-thirds of adults are interested in using voice assistants to arrange food orders.

Read the full blog at voicebot.ai.

IFA 2019 Takes Assistants Everywhere to a New Level

September 17, 2019

The IFA (Internationale Funkausstellung) Show occurred this past week in Berlin. It is certainly Europe’s largest tech show, and some would say it has now surpassed the Las Vegas Consumer Electronics Show (CES). I hit CES every year and this was my second IFA, but something radically changed this year in the world of Voice Assistants. I’m going to make up a conversation based on 2 imaginary people. For fun I’ll call them Sundar and Jeff:

Read the full blog at voicebot.ai.

Identifying Sounds as Accurately as Wake Words

August 21, 2019

At a recent meeting Sensory was credited for “inventing the wake word”. I explained that Sensory certainly helped to evangelize and popularize it, but we didn’t “invent” it. What we really did was substantially improve upon the state of the art so that it became useable. And it was a VERY hard challenge since we did it in an era before deep learning allowed us to further improve the performance.

Today Sensory is taking on the challenge of sound and scene identification. There are many dozens of companies working on this challenge…and it’s another HUGE challenge. There are some similarities with wake words and dealing with speech but a lot of differences too! I’m writing this to provide an update on our progress, to share some of our techniques, compare a bit with wake words and speech, and to bring more clear metrics to the table to look at accuracy!

Sensory announced our initial SoundID solution at CES 2019 here.

Since then we have been working on accuracy improvements and adding gunshot identification into the mix of our sounds (CO2 and smoke alarms, glass break, baby cry, snoring, door knock/bell, scream/yell, etc.) to be identified.

1.  General Approach. Sensory is using its TrulySecure Speaker Verification platform for sound ID. This approach using proprietary statistical and shallow learning techniques runs smaller models on device. It also uses a wider bandwidth filtering approach as it is intended to differentiate speech and sounds as opposed to simply recognizing words.
   1. A 2nd stage approach can be applied to improve accuracy. This second stage uses a DeepNet and can also run on device or in the cloud. It is more MIPS and memory intensive but by using the first stage power consumption is easily managed, and the first stage can be more accepting while the 2nd stage eliminates false alarms
      1. Second Stage (Deep Neural Network) eliminates 95% of false alarms from the first stage, while passing 97% of the real events.
      2. This enables to tune to the desired operating point (1 FA/day, .5 FAs/day, etc…)
      3. FR rate stays extremely low (despite of FA reduction) due to very accurate deep neural network and a “loose” first stage that is less discriminative
   2. Second stage classifier (deep neural network) is trained on many target sound examples. In order to separate target events from similar sounding non-target events we apply proprietary algorithmic and model building approaches to remove false alarms
   3. Combined model (1st and 2nd stage) smaller than 5 MB
   4.  Does a 3rd stage make sense? Sensory uses its TrulyHandsfree (THF) technology performing key word spotting for its wake words, and often transfers to TrulySecure for higher performance speaker verification. This allows wake words to be listened for at the lowest possible power consumption. Sensory is now exploring using THF as an initial stage for Sound ID to enable a 3 stage approach with the best in accuracy and the best in power consumption. This way power consumption can average less than 2 milliamps.
2. Testing Results. Here’s a few important findings that effect our test results:
   1. The difference between a quiet and noisy environment is quite pronounced. It’s easy to perform well in quiet and very difficult to perform great in noise, and it’s a different challenge than we faced with speech recognition, as the noises we are looking to identify can cover a much wider range of frequencies that can more closely match background noises. There’s a very good reason that when Alexa listens for glass break sounds, she does it in an “away” mode…that is when the home is quiet!! (Kudos to Amazon for the clever approach!). The results we will report all use noise-based testing. Spoiler alert…Sensory kicks ass! In our Alexa test simple drum beats and music caused glassbreaks to be detected. Sensory’s goal is avoiding this!
   2. Recorded sound effects are quite different than they sound live. The medium of playback (mobile phone vs PC vs high end speaker) can have a very big impact on the frequency spectrum and the ability to identify a sound. Once again this is quite different than human speech which falls into a relatively narrow frequency band and isn’t as affected by the playback mechanism. For testing, Sensory is using only high-quality sound playback.
   3. Some sounds are repeated others aren’t. This can have a huge effect on false rejects where the sound isn’t properly identified. It can be a “free” second chance to get it right. But this ability varies from sound to sound, for example, a glass break probably happens just once and it is absolutely critical to catch it; whereas a dog bark or baby cry happening once and not repeating may be unimportant and OK to ignore. We will show the effect on repeated sounds on accuracy tests.
   4. Our 2 stage approach works great. All the results shown are the performance of the 2 stage side by side.

• This at 1 FA in 24 hours on balanced mix of noise data. We tend to work on the sounds until we exceed 90% accuracy with 1 FA/day. So its no surprise that they hover in the same percentage region…some of these took more work than others. ;-)

• Once again at 1 FA in 24 hours on balanced mix of data. You can see how detection accuracy drops as noise levels grow. Of course we could tradeoff FA and FR to not drop performance so rapidly, and as the chart below shows we can also improve performance by requiring multiple events.

• Assuming 1 FA in 24 hours on balanced mix of data. The general effects of multiple instances hold true across sound ID categories. So for things like repeated dog barks or baby cries the solution can be very accurate. As a dog owner, I really wouldn’t want to be notified if my dark barked once or twice in a minute, but if it barked 10 times within a minute it might be more indicative of an issue I want to be notified about. Devices with Sensory technology can allow parametric controls of the number of instances to cause a notification.

Sensory is very proud of our progress in sound identification. We welcome and encourage others to share their accuracy reporting…I couldn’t find much online to determine “state of the art”.

Now we will begin work on scene analysis…and I expect Sensory to lead in this development as well!

Revisiting Wake Word Accuracy and Privacy

June 11, 2019

I used to blog a lot about wake words and voice triggers. Sensory pioneered this technology for voice assistants, and we evangelized the importance of not hitting buttons to speak to a voice recognizer. Then everybody caught on and the technology went into main stream use (think Alexa, OK Google, Hey Siri, etc.), and I stopped blogging about it. But I want to reopen the conversation…partly to talk about how important a GREAT wake word is to the consumer experience, and partly to congratulate my team on a recent comparison test that shows how Sensory continues to have the most accurate embedded wake word solutions.

Competitive Test Results. The comparison test was done by Vocalize.ai. Vocalize is an independent test house for voice enabled products. For a while, Sensory would contract out to them for independent testing of our latest technology updates. We have always tested in-house but found that our in-house simulations didn’t always sync up with our customers’ experience. Working with Vocalize allowed us to move from our in-house simulations to more real-world product testing. We liked Vocalize so much that we acquired them. So, now we “contract in” to them but keep their data and testing methodology and reporting uninfluenced by Sensory.

Vocalize compared two Sensory TrulyHandsfree wake word models (1MB size and 250KB size) with two external wake words (Amazon and Kitt.ai’s Snowboy), all using “Alexa” as the trigger. The results are replicable and show that Sensory’s TrulyHandsfree remains the superior solution on the market. TrulyHandsfree was better/lower on BOTH false accepting AND false rejecting. And in many cases our technology was better by a longshot! If you would like see the full report and more details on the evaluation methods, please send an email request to either Vocalize (dev@vocalize.ai) or Sensory (sales@sensory.com).

It’s Not Easy. There are over 20 companies today that offer on-device wake words. Probably half of these have no experience in a commercially shipping product and they never will; there are a lot of companies that just won’t be taken seriously. The other half can talk a good talk, and in the right environment they can even give a working demo. But this technology is complex, and really easy to do badly and really hard to do great. Some demos are carefully planned with the right noise in the right environment with the right person talking. Sensory has been focused on low power embedded speech for 25 years, we have 65 of the brightest minds working on the toughest challenges in embedded AI. There’s a reason that companies like Amazon, Google, Microsoft and Samsung have turned to Sensory for our TrulyHandsfree technology. Our stuff works, and they understand how difficult it is to make this kind of technology work on-device! We are happy to provide APK’s so you can do you’re your own testing and judge for yourself! OK, enough of the sales pitch…some interesting stuff lays ahead…

It’s Really Important. Getting a wake word to work well is more important than most people realize. It’s like the front door to your house. It might be a small part of your house, but if you aren’t letting the homeowners in then that’s horrible, and if you are letting strangers in by accident that’s even worse. The name a company gives their wake word is usually the company brand name, imagine the sentiment that comes off when I say a brand name and it doesn’t work. Recently I was at a tradeshow that had a Mercedes booth. There were big signs that said “Hey Mercedes”…I walked up to the demo area and I said “Hey Mercedes” but nothing happened…the woman working there informed me that they couldn’t demo it on the show floor because it was really too noisy. I quickly pulled out my mobile phone and showed her that I could use dozens of wake words and command sets without an error in that same environment. Mercedes has spent over 100 years building up one of the best quality brand reputations in the car industry. I wonder what will happen to that reputation if their wake word doesn’t respond in noise? Even worse is when devices accidentally go off. If you have family members that listen to music above volume 7 then you already know the shock that a false alarm causes!

It’s about Privacy. Amazon, like Google and a few others seem to have pretty good wake words, but if you go into your Alexa settings you can see all of the voice data that’s been collected, and a lot of it is being collected when you weren’t intentionally talking to Alexa! You can see this performance issue in the Vocalize test report. Sensory substantially outperformed Amazon in the false reject area. This is when a person tries to speak to Alexa and she doesn’t respond. The difference is most apparent in babble noise where Sensory falsely rejected 3% and Amazon falsely rejected 10% in comparable sized models (250KB). However the False Accept difference is nothing short of AMAZING. Amazon false accepted 13 times in 24 hours of random noise. In this same time period Sensory false accepted ZERO times (on comparably sized 250KB models). How is this possible you may be wondering? Amazon “fixes” its mistakes in the cloud. Even though the device falsely accepts quite frequently, their (larger and more sophisticated) models in the cloud collect the error. Was that a Freudian slip? They correct the error…AND they COLLECT the error. In effect, they are disregarding privacy to save device cost and collect more data.

As the voice revolution continues to grow, you can bet that privacy will continue to be a hot topic. What you now understand is that wake word quality has a direct impact on both the user experience and PRIVACY! While most developers and product engineers in the CE industry are aware of wake words and the difficulty in making them work well on-device, they don’t often consider that competing wake words technologies aren’t created equally – the test results from Vocalize prove it! Sensory is more accurate AND allows more privacy!

Taking Back Control of Our Personal Data

March 11, 2019

As more and more devices that are designed to watch and listen to us flood the market, there is rising concern about how the personal data that are collected gets used. Facebook has admitted to monetizing our personal data and the personal data of our friends. Google has been duplicitous in its tracking of users even when privacy settings are set to not track, and more recently has admitted to placing microphones in products without informing consumers. In no realm is the issue of privacy more relevant than today’s voice assistants. A recent PC Magazine survey of over 2,000 people found that privacy was the top concern for smart home devices (more important than cost!). The issue becomes more complex as personal assistant devices become increasingly better-equipped with IP cameras and various other sensors to watch, track and listen to us.

Admittedly, people get a lot in return for giving up their privacy. We’ve become numb to privacy policies. It is almost expected that we will tap “accept” on End User License Agreements (EULAs) without reading through terms that are intentionally designed to be difficult to read. We get free software, free services, music and audio feeds, discounted rates and other valuable benefits. What we don’t fully realize is what we are giving up, because big data companies don’t openly share that with us. We fall into two main categories of consumers: trust the giants of industry to do what’s right, or don’t play (and lose out on the benefits). For example, I love having smart speakers in my home, but many of my friends won’t get them — precisely because of these very fair privacy concerns. Our legislators are trying to address privacy concerns, but the government needs to fully understand how data is used before writing legislation. They need help from the tech community on how to deal with these issues. Europe has adopted the new General Data Protection Regulation (GDPR) to address concerns related to businesses’ handling and protection of user data. This is a step in the right direction, as it provides clear rules to companies that handle personal data and offers clearly defined monetary penalties for failure to protect the data of citizens. However, is it enough to slap fines on these companies for failing to comply? What happens when they think the market value of the infringement will be greater than the penalty? Or when human errors are made, which happened when a European user requested their data and was accidentally given someone else’s data.

In this day and age, a great deal of our daily tasks require us to share personal data. Whether we are talking to our AI assistants, using our smart phone to navigate around traffic or making a credit card transaction at a local retailer, we’re sharing data. There are benefits to sharing this data, and as time goes on people will see even more benefits from data sharing, but there are so many problems too.

AI personal assistants provide examples of how sharing data can benefit or hurt the end user. The more we use these devices the more they learn about us. As they become capable of recognizing who we are by face or voice and get to know and memorize our histories, preferences and needs, these systems will evolve from devices that answer simple questions into proactive, accurate helpers that offer useful recommendations, unprompted reminders, improved home security — assistance we users find truly helpful. But they can also reveal private information that we don’t want others to know, which is what happened when a girl’s shopping habits triggered advertising for baby products before her parents knew she was pregnant.

As digital assistant technologies get smarter, there will be more concern about what private data they collect, store and share over the internet. One solution to the privacy problem would be keeping whatever is learned by the device on the device by moving the AI processing out of the cloud to the edge (on device), ensuring our assistants never take personal data to the cloud so there is no privacy risk. This would be a good solution as embedded AI becomes more powerful. The biggest disadvantage may be that every new device would have to re-learn who we are.

Another possible solution is more collaboration among data companies. Some of the AI assistant companies are starting to work together; for example, Cortana and Alexa are playing friendly. This current approach, however, is about transferring the baton from one assistant to another so that multiple assistants can be accessed from a single device. It’s unlikely this approach will be widely adopted, and even if it were, it would result in inefficiency in the collection and use of our personal data, because each of the AI assistant providers would have to build their own profile of who we are based on the data they collect. However, because companies will want to eventually monetize the data they collect, sharing done right could actually benefit us.

Could more sharing of our data and preferences improve AI assistants?

Sharing data does create the best and most consistent user experience. It allows consumers to switch AI brands or buy new devices without losing any of the benefits in user experience and device knowledge of who we are. Each assistant having its own unique data and profile for its users skirts the privacy issue and misses the advantages of big data. That doesn’t seem to be the right way for the industry to advance. Ideally, we would create a system where there is shared knowledge of who we are, but we, as individuals, need to control and manage that knowledge.

For example, Google knows that the most frequent search I do on Google Maps is for vegetarian restaurants. Google has probably figured out that I’m a vegetarian. Quite ironically, I found “meat” in my shopping cart at Amazon. Somehow, Alexa thought I asked it to add “meat” to my shopping cart. Perhaps if Alexa had the same knowledge of me as Google it would have made a different and better decision. Likewise, Amazon knows a lot about my shopping details. It knows what pills I take, my shoe size and a whole lot of other things that might be helpful to the Google Assistant. A shared knowledge base would benefit me, but I want to be able to control and oversee this shared database or profile.

Improved privacy without losing the advantages of shared data is achievable through a combination of private devices with embedded intelligence and cloud devices restricted and governed by legislature and industry standards.

Legislature should enable user-controlled personal-data sharing systems, then the user can choose their own mix of private devices with embedded intelligence and cloud devices that offer more general and standard protections. Of course the legislature is the hard part since our government tends to work more for the industries that fund reelections and bills than the people…nevertheless here are some thoughts on how legislature for privacy could work:

1. 1. Companies can’t retain an individual’s private information. That’s it! There is no good reason for them to have it. Period….but they can collect it and provide it to the owner.
   2. Every person has and controls their “Shared Profile” or SP. The SP contains any and all information collected from any company. It is an item list categorized by type (clothing sizes, restaurants, etc.).
   3. The SP is divided into “confidential” (publicly available to any company) and “non-confidential” (not publicly available) sections. The SP owner has direct control over their data and decides the following:
      • Which categories reside in the “non-confidential” SP vs. the “confidential” SP
      • Which items are included in those public categories inside the “non-confidential” SP
      • Which companies can access information in the “confidential” SP and what specific information they have access to
   4. Companies can access the “non-confidential” SP and utilize it for their purposes, but they can’t disclose or sell anything in it to others. It’s our data, not theirs!
   5. Companies can create new categories or items within categories on our SP; however, data added by companies is automatically placed in the “confidential” section. The user gets notified about new information and they can monitor, screen, edit and move such information into the “non-confidential” SP, making it useful for other companies to access.

Ultimately, this concept is a shared format designed to give companies equal access to data while providing users full ownership and control of their personal data and a clear understanding of what is shared or made public. No longer would the user have to deal with individual companies and complex EULAs, and no longer would they need to fear that their devices are carrying out unscrupulous behavior because of the vagueness of our legal system and the less-than-transparent ways big-data companies use our data.

With a little creative thinking and a shift in regulation, we the people can change the future of data collection and take back ownership and control of our personal data.

The Move Towards On-Device Assistants for Performance and Privacy

February 11, 2019

Voice Assistants are growing in both popularity and capability. They are arriving in our home, cars, mobile devices and seem to now be a standard part of American culture, entering our tv shows, movies, music, and Super Bowl ads. However, this popularity is accompanied by a persistent concern over our privacy and the safety of our personal data when these devices are always listening and always watching.

There is a significant distrust of big companies like Facebook, Google, Apple, and Amazon. Facebook and Google have admitted to misusing our private data, and Apple and Amazon have admitted that system failures have led to a loss of private data.

So naturally, there would be an advantage of not sending our voices or videos into the cloud and doing the processing on-device. Then no data loss is at risk. Cloud-based queries could still occur, but through anonymized text only.

COMPUTING AT THE EDGE VERSUS THE CLOUD
There are forces bringing us closer to edge-based assistants and there are other forces leading to data going through the cloud. Here are a few ideas to consider.

• Power and Memory. There is no doubt that cloud-based solutions offer more power and memory, and deep learning approaches can certainly take advantage of those features. However, access speed and available bandwidth are often issues giving an edge to working on-device. Current, state of the art deep net modeling can allow limited domain natural language engines that require substantially less memory and MIPS than general purpose models, making natural language on device realistic today. Furthermore, powerful on-device voice experiences are increasingly realistic as we pack more and more memory and MIPS into smaller and cheaper packages. New chip architectures targeting deep learning methodologies can also lead to on-device breakthroughs and these designs are now hitting the markets.
• Accuracy. Although power and memory may be key factors in influencing accuracy, an on-device assistant may be able to take advantage of sensor and usage data and other embedded information not available to the cloud-based assistant so that it can better adapt to users and their preferences.
• Privacy. Not sending data to the cloud is more private.

Some have argued that we have carried microphones and cameras around with us for years without any issues, but I see this thinking as flawed. Just recently, Apple admitted to a facetime bug on mobile phones enabling “eavesdropping” on others.

Also, if my phone is listening for a wake word it’s a very different technology model than an IoT device that’s “always on.” Phones are usually designed to listen in arms-length situations of 2 or 3 feet. An IoT speaker is designed to listen to 20 feet! If we assume constant noise across a room that could make an assistant “false fire” and start listening, then we can think of 2 listening circles, one with a radius of 3 feet and one with a radius of 20 feet, to compare the listening area of the phone with a far-field IoT device such as a smart speaker. The phone has a listening area of π r2 or 9 π, the IoT device has a listening area of 400 π. So, all else equal the IoT device is about 44 times more likely to false fire and start listening when it wasn’t intended to.

As cloud-based far-field assistants enter the home there is a definite risk of our private data getting intercepted. It’s not just machine errors but human errors too, like the Amazon employee that accidentally sent out the wrong data to a person that requested it.

There are also other means in which we can lose our cloud-connected private data like the “dolphin attack” that can allow outsiders to listen in.

• The will of Amazon, Google, Apple, Government, and others. We should not underestimate the market power and persuasiveness of these tech giants. They want to open our wallets, and the best way to do that is to present us with things we want to buy…whether, food, shelter, gifts or whatever. Amazon is pretty good at selling us stuff. Google is pretty good at making money connecting people with things they want and showing them ads. User data makes all of these things easier and more effective. More effective means they make more money showing us ads and selling us stuff. I suspect that most of these giant players will have strong incentives to keep our assistants and our data flowing into the cloud. Of course, tempering this will is the various govt agencies trying to protect consumer privacy. Europe has launched GDPR (ironically leading to the Amazon accident mentioned above!) which could provide some disincentives around using cloud-based services.

ON-DEVICE VOICE ASSISTANTS WILL BECOME MORE COMMON
My conclusion is that there is a lot of opportunity in bringing assistants onto devices. It can not only protect privacy but through adaptation and domain limitation it can create a better-customized user experience. I predict increasingly more products to use on-device voice control and assistants! Of course, I also predict increasingly more devices to use cloud assistants. What wins out, in the long run, will probably depend more on government legislation and individual privacy concerns than anything else.

Sensory CEO On Voice-Activated Technology’s Next Big Wave

January 11, 2019

Interview with Karen Webster, one of the best writers and interviewers in tech/fintech.

In 1994 the fastest imaginable connection to the internet was a 28.9 kbps dial-up modem and email was still mostly a new thing that many people were writing off as a fad. There was no such thing as Amazon.com for the first half the year and less than a third of American households owned computers. Given that, it’s not much of a surprise that the number of people thinking about voice-activated, artificial intelligence (AI)-enhanced wireless technology was extremely small — roughly the same as the number of people putting serious thought into flying cars.

But the team at Sensory is not quite as surprised by the rapid onset evolution of the voice-activated technology marketplace as everyone else may be — because when they were first opening their doors 25 years ago in 1994, this is exactly the world they had hoped to see developing two-and-a-half decades down the line, even if the progress has been a bit uneven.

“We still have a long way to go,” Sensory CEO Todd Mozer told Karen Webster in a recent conversation. “I am excited about how good speech recognition has gotten, but natural language comprehension still needs a lot of work. And combined the inputs of all the sensors devices have — for vision and speech together to make things really smart and functional in context — we just aren’t there yet.”

But for all there is still be to done, and advances that still need to be made, the simple fact that the AI-backboned neural net approach to developing for interactive technology has become “more powerful than we ever imagined it would be with deep learning,” is a huge accomplishment in and of itself.

And the accomplishments are rolling forward, he noted, as AI’s reach and voice control of devices is expanding — and embedding — and the nascent voice ecosystem is quickly growing into its adolescent phase.

“Today these devices do great if I need the weather or a recipe. I think in the future they will be able to do far more than that — but they will be increasingly be invisible in the context of what we are otherwise doing.”

Embedding The Intelligence

Webster and Mozer were talking on the eve of the launch of Sensory’s VoiceGenie for Bluetooth speaker — a new product for speaker makers to add voice controls and functions like wake words, without needing any special apps or a Wi-Fi connection. Said simply, Mozer explained, what Sensor is offering for Bluetooth makers is embedded voice — instead of voice via connection to the cloud.

And the expansion into embedded AI and voice control, he noted, is necessary, particularly in the era of data breach, cyber-crime and good old-fashioned user error with voice technology due to its relative newness.

“There are a lot of sensors on our products and phones that are gathering a lot of interesting information about what we are doing and who we are,” Mozer said.

Apart from being a security problem to send all of that information to the cloud, embedding in devices the ability to extract usefully and adapt on demand to a particular user is an area of great potential in improving the devices we all use multiple times daily.

This isn’t about abandoning the cloud, or even a great migration away from it, he said; there’s always going to be a cloud and clients for it. The cloud natively has more power, memory and capacity than anything that can be put into a device at this point on a cost-effective basis.

“But there is going to be this back-and-forth and things right now are swinging toward more embedded ability on devices,” he said. “There is more momentum in that direction.”

The cloud, he noted, will always be the home of things like transactions, which will have to flow through it. But things like verification and authentication, he said, might be centered in the devices’ embedded capacity, as opposed to in the cloud itself.

The Power Of Intermediaries

Scanning the headlines of late in the world of voice connection and advancing AI, it is easy to see two powerful players emerging in Amazon and Google. Amazon announced Alexa’s presence on 100 million devices, and Google immediately followed up with an announcement of its own that Google Assistant will soon be available on over a billion devices.

Their sheer size and scale gives those intermediaries a tremendous amount of power, as they are increasingly becoming the connectors for these services on the way to critical mass and ubiquity, Webster remarked.

Mozer agreed, and noted that this can look a little “scary” from the outside looking in, particularly given how deeply embedded Amazon and Google otherwise are with their respective mastery of eCommerce and online search.

Like many complex ecosystems, Mozer said that the “giants” — Amazon, Google and Apple to a lesser extent — are both partners and competitors, adding that Sensory’s greatest value to the voice ecosystem is when something that is very customized tech and requires a high level of accuracy and customer service features is needed. Sensory’s technology appears in products by Google, Alibaba, Docomo and Amazon, to name a few.

But ultimately, he noted, the marketplace is heading for more consolidation — and probably putting more power in the hands of very few selected intermediaries.

“I don’t think we are going to have 10 different branded speakers. There will be some kind of cohesion — someone or maybe two someones will kick butt and dominate, with another player struggling in third place. And then a lot of players who aren’t players but want to be. We’ve seen that in other tech, I think we will see it with voice.”

As for who those winning players will be, Google and Amazon look good today, but, Mozer noted, it’s still early in the race.

The Future of Connectedness

In the long term future, Mozer said, we may someday look back on all these individual smart devices as a strange sort of clutter from the past, when everyone was making conversation with different appliances. At some point, he ventured, we may just have sensors embedded in our heads that allow us to think about commands and have them go through — no voice interface necessary

“That sounds like science fiction, but I would argue it is not as far out there as you think. It won’t be this decade, but it might be in the next 50 years.”

But in the more immediate — and less Space Age — future, he said, the next several years will be about enhancing and refining voice technologies ability to understand and respond to human voice — and, ultimately, to anticipate the needs of human users.

There won’t be a killer app for voice that sets it on the right path, according to Mozer; it will simply be a lot of capacity unlocked over time that will make voice controls the indispensable tools Sensory has spent the last 25 years hoping they would become.

“When a device is accurate in identifying who you are, and carrying out your desires seamlessly, that will be when it finds its killer function. It is not a thing that someone is going to snap their fingers and come out with,” he said, “it is going to be an ongoing evolution.”

Biometrics’ Place in the Embedded Industry

October 25, 2018

I just returned from the four-day Money 20/20 event in Las Vegas. The show covers the overlap of Money and Technology including FinTech, Payment, Ecommerce and more. It had tens of thousands of attendees, over 3,500 companies, and 400 startups and lots of starpower including Richard Branson, Shaquille O’Neil, Akon, and yours truly speaking on a biometrics panel.

I walked the show floor to find the latest news in embedded biometrics and to better understand the choice between embedded and cloud based biometrics in the fintech/money space. I was impressed by how biometrics has moved into the mainstream conversation. Before mentioning the other companies I talked to, I’ll kick off with Sensory, my company.

Sensory’s focus on AI and biometrics has always been on the embedded side. We believe in data privacy and we think the best way to accomplish that is through keeping in the hands and control of the user. On a less promotional front there is also a strategic reason we focus on embedded, and that’s because the industry giants are really good at cloud based and unconstrained AI tasks, and they often give it away for free, so we are focused on a place where the Googles and Amazons of the world can be our customers and not just our competitors. On the last day of Money 20/20, Sensory introduced TrulySecure 4.0, a fusion of face and voice biometrics with improved accuracy, speed, and support for 3D.

BioConnect sponsored one of the excellent lunches at the show. I spoke to Rob Douglas, Founder and CEO of BioConnect who said, “We are on the quest for rightful identity and what we offer is a market leading mobile biometric authentication solution for the enterprise. We provide a building block like a piece of LEGO that you can apply into all the infrastructure of an Enterprise to upgrade from passwords and key fobs to a world where you have higher assurance when you are conducting digital or physical transactions.” BioConnect has been in business for eight years and has 1,600 customers and at Money 20/20, the Bank of Montreal announced a partnership with BioConnect and IBM.

BioConnect has a strong belief in face authentication, but also works with other biometrics including voice, eye, fingerprint, and behavioral. According to Douglas, “We believe in both cloud and client and we support the FIDO approach, but there are use cases where the transport of the biometrics through a cloud-based infrastructure can make a lot of sense.”

The FIDO Alliance had a large area with alliance members touting their wares. FIDO (fast identity online) is “the World’s Largest Ecosystem for Standards-Based, Interoperable Authentication.” I spoke to Andrew Shikiar, the CMO of the FIDO Alliance. Local authentication with biometrics is key to the FIDO approach. “Whether you are storing passwords or biometrics, a central repository will be targeted, and will be breached to be used in nefarious ways.” When I asked Shikiar about the desire to share biometrics across platforms he said, “That’s typical of the type of use case that our technical working groups are working to address, while leveraging the FIDO standards”

Conor White, President Americas at Daon described Daon as “a human authentication company that provides technologies to allow customers to create and manage digital identities of their users in a way that’s advantageous in a risk and security perspective.” At the show they announced a partnership to expand from their base in mobile into the contact center.

Daon provides support to a wide cross section of biometrics and provides embedded solutions through the FIDO standard but can support cloud based biometrics when desired. Daon is seeing more customers getting comfortable with going from on premise to cloud based implementations but in the vast majority of cases, the biometrics still resides on the device even if the service is run in the cloud. White sits on the board of the FIDO alliance and sees the FIDO standard with embedded biometrics gaining ground.

Veritran is a software company based in Buenos Aires and developing innovative and secure digital banking platforms for the Latin American markets. They process over 4 billion banking transactions each year, and they are now expanding from Banking into other Enterprise markets and geographies beyond Latin America. At Mobile World Congress in February, they announced a new platform for secure application development, and at Money 20/20 ,they demonstrated some of the apps developed on this platform.

Like other companies, Veritran offers a mix of biometric modalities and in talking with Veritran’s CEO Marcelo Gonzales, I learned a very interesting reason as to why they prefer embedded biometrics instead of processing in the cloud. The Latin American customers buy prepaid plans with limited data. To keep their costs down, they must keep their data usage down, and with the biometrics stored and processed on the device, transactions can occur with minimal data costs.

There were a lot of other companies at Mobile 20/20. As a quick summary I would say a few important things stood out. Biometrics are definitely taking off as we all understand the problems with passwords. A variety of biometric modalities are offered but there does seem to be a preference and movement toward face authentication that can run cross platform without specialized hardware. Most vendors offer a choice between having the biometric data stored and processed on the device or in the cloud, but with the FIDO Alliance behind embedded and the clear advantages for security and privacy, the embedded usage case seems to be winning out.

IFA 2018 Becomes a Showcase for Amazon and Google Voice Assistants in Berlin

September 13, 2018

I thought it was funny that Google and Alexa both handed out the neck worn badge holders which nobody seemed to wear.