Digging deeper into smart speakers reveals two clear paths

Digging deeper into smart speakers reveals two clear paths
In a truly fascinating exploration into two smart speakers – the Sonos One and the Amazon Echo – BoltVC’s Ben Einstein has found some interesting differences in the way a traditional speaker company and an infrastructure juggernaut look at their flagship devices.
The post is well worth a full read but the gist is this: Sonos, a very traditional speaker company, has produced a good speaker and modified its current hardware to support smart home features like Alexa and Google Assistant. The Sonos One, notes Einstein, is a speaker first and smart hardware second.
“Digging a bit deeper, we see traditional design and manufacturing processes for pretty much everything. As an example, the speaker grill is a flat sheet of steel that’s stamped, rolled into a rounded square, welded, seams ground smooth, and then powder coated black. While the part does look nice, there’s no innovation going on here,” he writes.
The Amazon Echo, on the other hand, looks like what would happen if an engineer was given an unlimited budget and told to build something that people could talk to. The design decisions are odd and intriguing and it is ultimately less a speaker than a home conversation machine. Plus it is very expensive to make.
Pulling off the sleek speaker grille, there’s a shocking secret here: this is an extruded plastic tube with a secondary rotational drilling operation. In my many years of tearing apart consumer electronics products, I’ve never seen a high-volume plastic part with this kind of process. After some quick math on the production timelines, my guess is there’s a multi-headed drill and a rotational axis to create all those holes. CNC drilling each hole individually would take an extremely long time. If anyone has more insight into how a part like this is made, I’d love to see it! Bottom line: this is another surprisingly expensive part.

Sonos, which has been making a form of smart speaker for 15 years, is a CE company with cachet. Amazon, on the other hand, sees its devices as a way into living rooms and a delivery system for sales and is fine with licensing its tech before making its own. Therefore to compare the two is a bit disingenuous. Einstein’s thesis that Sonos’ trajectory is troubled by the fact that it depends on linear and closed manufacturing techniques while Amazon spares no expense to make its products is true. But Sonos makes speakers that work together amazingly well. They’ve done this for a decade and a half. If you compare their products – and I have – with competing smart speakers an non-audiophile “dumb” speakers you will find their UI, UX, and sound quality surpass most comers.
Amazon makes things to communicate with Amazon. This is a big difference.
Where Einstein is correct, however, is in his belief that Sonos is at a definite disadvantage. Sonos chases smart technology while Amazon and Google (and Apple, if their HomePod is any indication) lead. That said, there is some value to having a fully-connected set of speakers with add-on smart features vs. having to build an entire ecosystem of speaker products that can take on every aspect of the home theatre.
On the flip side Amazon, Apple, and Google are chasing audio quality while Sonos leads. While we can say that in the future we’ll all be fine with tinny round speakers bleating out Spotify in various corners of our room, there is something to be said for a good set of woofers. Whether this nostalgic love of good sound survives this generation’s tendency to watch and listen to low resolution media is anyone’s bet, but that’s Amazon’s bet to lose.
Ultimately Sonos is strong and fascinating company. An upstart that survived the great CE destruction wrought by Kickstarter and Amazon, it produces some of the best mid-range speakers I’ve used. Amazon makes a nice – almost alien – product, but given that it can be easily copied and stuffed into a hockey puck that probably costs less than the entire bill of materials for the Amazon Echo it’s clear that Amazon’s goal isn’t to make speakers.
Whether the coming Sonos IPO will be successful depends partially on Amazon and Google playing ball with the speaker maker. The rest depends on the quality of product and the dedication of Sonos users. This good will isn’t as valuable as a signed contract with major infrastructure players but Sonos’ good will is far more than Amazon and Google have with their popular but potentially intrusive product lines. Sonos lives in the home while Google and Amazon want to invade it. That is where Sonos wins.

Source: Gadgets – techcrunch

Your next summer DIY project is an AI-powered doodle camera

Your next summer DIY project is an AI-powered doodle camera
With long summer evenings comes the perfect opportunity to dust off your old boxes of circuits and wires and start to build something. If you’re short on inspiration, you might be interested in artist and engineer Dan Macnish’s how-to guide on building an AI-powered doodle camera using a thermal printer, Raspberry pi, a dash of Python and Google’s Quick Draw data set.
“Playing with neural networks for object recognition one day, I wondered if I could take the concept of a Polaroid one step further, and ask the camera to re-interpret the image, printing out a cartoon instead of a faithful photograph.” Macnish wrote on his blog about the project, called Draw This.
To make this work, Macnish drew on Google’s object recognition neural network and the data set created for the game Google Quick, Draw! Tying the two systems together with some python code, Macnish was able to have his creation recognize real images and print out the best corresponding doodle in the Quick, Draw! data set
But since output doodles are limited to the data set, there can be some discrepancy between what the camera “sees” and what it generates for the photo.
“You point and shoot – and out pops a cartoon; the camera’s best interpretation of what it saw,” Macnish writes. “The result is always a surprise. A food selfie of a healthy salad might turn into an enormous hot dog.”
If you want to give this a go for yourself, Macnish has uploaded the instructions and code needed to build this project on GitHub.

Source: Gadgets – techcrunch

Apple is rebuilding Maps from the ground up

Apple is rebuilding Maps from the ground up

I’m not sure if you’re aware, but the launch of Apple Maps went poorly. After a rough first impression, an apology from the CEO, several years of patching holes with data partnerships and some glimmers of light with long-awaited transit directions and improvements in business, parking and place data, Apple Maps is still not where it needs to be to be considered a world class service.

Maps needs fixing.

Apple, it turns out, is aware of this, so It’s re-building the maps part of Maps.

It’s doing this by using first-party data gathered by iPhones with a privacy-first methodology and its own fleet of cars packed with sensors and cameras. The new product will launch in San Francisco and the Bay Area with the next iOS 12 Beta and will cover Northern California by fall.

Every version of iOS will get the updated maps eventually and they will be more responsive to changes in roadways and construction, more visually rich depending on the specific context they’re viewed in and feature more detailed ground cover, foliage, pools, pedestrian pathways and more.

This is nothing less than a full re-set of Maps and it’s been 4 years in the making, which is when Apple began to develop its new data gathering systems. Eventually, Apple will no longer rely on third-party data to provide the basis for its maps, which has been one of its major pitfalls from the beginning.

“Since we introduced this six years ago — we won’t rehash all the issues we’ve had when we introduced it — we’ve done a huge investment in getting the map up to par,” says Apple SVP Eddy Cue, who now owns Maps in an interview last week.  “When we launched, a lot of it was all about directions and getting to a certain place. Finding the place and getting directions to that place. We’ve done a huge investment of making millions of changes, adding millions of locations, updating the map and changing the map more frequently. All of those things over the past six years.”

But, Cue says, Apple has room to improve on the quality of Maps, something that most users would agree on, even with recent advancements.

“We wanted to take this to the next level,” says Cue. “We have been working on trying to create what we hope is going to be the best map app in the world, taking it to the next step. That is building all of our own map data from the ground up.”

In addition to Cue, I spoke to Apple VP Patrice Gautier and over a dozen Apple Maps team members at its mapping headquarters in California this week about its efforts to re-build Maps, and to do it in a way that aligned with Apple’s very public stance on user privacy.

If, like me, you’re wondering whether Apple thought of building its own maps from scratch before it launched Maps, the answer is yes. At the time, there was a choice to be made about whether or not it wanted to be in the business of Maps at all. Given that the future of mobile devices was becoming very clear, it knew that mapping would be at the core of nearly every aspect of its devices from photos to directions to location services provided to apps. Decision made, Apple plowed ahead, building a product that relied on a patchwork of data from partners like TomTom, OpenStreetMap and other geo data brokers. The result was underwhelming.

Almost immediately after Apple launched Maps, it realized that it was going to need help and it signed on a bunch of additional data providers to fill the gaps in location, base map, point-of-interest and business data.

It wasn’t enough.

“We decided to do this just over four years ago. We said, “Where do we want to take Maps? What are the things that we want to do in Maps? We realized that, given what we wanted to do and where we wanted to take it, we needed to do this ourselves,” says Cue.

Because Maps are so core to so many functions, success wasn’t tied to just one function. Maps needed to be great at transit, driving and walking — but also as a utility used by apps for location services and other functions.

Cue says that Apple needed to own all of the data that goes into making a map, and to control it from a quality as well as a privacy perspective.

There’s also the matter of corrections, updates and changes entering a long loop of submission to validation to update when you’re dealing with external partners. The Maps team would have to be able to correct roads, pathways and other updating features in days or less, not months. Not to mention the potential competitive advantages it could gain from building and updating traffic data from hundreds of millions of iPhones, rather than relying on partner data.

Cue points to the proliferation of devices running iOS, now numbering in the millions, as a deciding factor to shift its process.

“We felt like because the shift to devices had happened — building a map today in the way that we were traditionally doing it, the way that it was being done — we could improve things significantly, and improve them in different ways,” he says. “One is more accuracy. Two is being able to update the map faster based on the data and the things that we’re seeing, as opposed to driving again or getting the information where the customer’s proactively telling us. What if we could actually see it before all of those things?”

I query him on the rapidity of Maps updates, and whether this new map philosophy means faster changes for users.

“The truth is that Maps needs to be [updated more], and even are today,” says Cue. “We’ll be doing this even more with our new maps, [with] the ability to change the map real-time and often. We do that every day today. This is expanding us to allow us to do it across everything in the map. Today, there’s certain things that take longer to change.

“For example, a road network is something that takes a much longer time to change currently. In the new map infrastructure, we can change that relatively quickly. If a new road opens up, immediately we can see that and make that change very, very quickly around it. It’s much, much more rapid to do changes in the new map environment.”

So a new effort was created to begin generating its own base maps, the very lowest building block of any really good mapping system. After that, Apple would begin layering on living location data, high resolution satellite imagery and brand new intensely high resolution image data gathered from its ground cars until it had what it felt was a ‘best in class’ mapping product.

There is only really one big company on earth who owns an entire map stack from the ground up: Google .

Apple knew it needed to be the other one. Enter the vans.

Apple vans spotted

Though the overall project started earlier, the first glimpse most folks had of Apple’s renewed efforts to build the best Maps product was the vans that started appearing on the roads in 2015 with ‘Apple Maps’ signs on the side. Capped with sensors and cameras, these vans popped up in various cities and sparked rampant discussion and speculation.

The new Apple Maps will be the first time the data collected by these vans is actually used to construct and inform its maps. This is their coming out party.

Some people have commented that Apple’s rigs look more robust than the simple GPS + Camera arrangements on other mapping vehicles — going so far as to say they look more along the lines of something that could be used in autonomous vehicle training.

Apple isn’t commenting on autonomous vehicles, but there’s a reason the arrays look more advanced: they are.

Earlier this week I took a ride in one of the vans as it ran a sample route to gather the kind of data that would go into building the new maps. Here’s what’s inside.

In addition to a beefed up GPS rig on the roof, four LiDAR arrays mounted at the corners and 8 cameras shooting overlapping high-resolution images – there’s also the standard physical measuring tool attached to a rear wheel that allows for precise tracking of distance and image capture. In the rear there is a surprising lack of bulky equipment. Instead, it’s a straightforward Mac Pro bolted to the floor, attached to an array of solid state drives for storage. A single USB cable routes up to the dashboard where the actual mapping capture software runs on an iPad.

While mapping, a driver…drives, while an operator takes care of the route, ensuring that a coverage area that has been assigned is fully driven and monitoring image capture. Each drive captures thousands of images as well as a full point cloud (a 3D map of space defined by dots that represent surfaces) and GPS data. I later got to view the raw data presented in 3D and it absolutely looks like the quality of data you would need to begin training autonomous vehicles.

More on why Apple needs this level of data detail later.

When the images and data are captured, they are then encrypted on the fly immediately and recorded on to the SSDs. Once full, the SSDs are pulled out, replaced and packed into a case which is delivered to Apple’s data center where a suite of software eliminates private information like faces, license plates and other info from the images. From the moment of capture to the moment they’re sanitized, they are encrypted with one key in the van and the other key in the data center. Technicians and software that are part of its mapping efforts down the pipeline from there never see unsanitized data.

This is just one element of Apple’s focus on the privacy of the data it is utilizing in New Maps.

Probe data and Privacy

Throughout every conversation I have with any member of the team throughout the day, privacy is brought up, emphasized. This is obviously by design as it wants to impress upon me as a journalist that it’s taking this very seriously indeed, but it doesn’t change the fact that it’s evidently built in from the ground up and I could not find a false note in any of the technical claims or the conversations I had.

Indeed, from the data security folks to the people whose job it is to actually make the maps work well, the constant refrain is that Apple does not feel that it is being held back in any way by not hoovering every piece of customer-rich data it can, storing and parsing it.

The consistent message is that the team feels it can deliver a high quality navigation, location and mapping product without the directly personal data used by other platforms.

“We specifically don’t collect data, even from point A to point B,” notes Cue. “We collect data — when we do it —in an anonymous fashion, in subsections of the whole, so we couldn’t even say that there is a person that went from point A to point B. We’re collecting the segments of it. As you can imagine, that’s always been a key part of doing this. Honestly, we don’t think it buys us anything [to collect more]. We’re not losing any features or capabilities by doing this.”

The segments that he is referring to are sliced out of any given person’s navigation session. Neither the beginning or the end of any trip is ever transmitted to Apple. Rotating identifiers, not personal information, are assigned to any data or requests sent to Apple and it augments the ‘ground truth’ data provided by its own mapping vehicles with this ‘probe data’ sent back from iPhones.

Because only random segments of any person’s drive is ever sent and that data is completely anonymized, there is never a way to tell if any trip was ever a single individual. The local system signs the IDs and only it knows who that ID refers to. Apple is working very hard here to not know anything about its users. This kind of privacy can’t be added on at the end, it has to be woven in at the ground level.

Because Apple’s business model does not rely on it serving, say, an ad for a Chevron on your route to you, it doesn’t need to even tie advertising identifiers to users.

Any personalization or Siri requests are all handled on-board by the iOS device’s processor. So if you get a drive notification that tells you it’s time to leave for your commute, that’s learned, remembered and delivered locally, not from Apple’s servers.

That’s not new, but it’s important to note given the new thing to take away here: Apple is flipping on the power of having millions of iPhones passively and actively improving their mapping data in real time.

In short: traffic, real-time road conditions, road systems, new construction and changes in pedestrian walkways are about to get a lot better in Apple Maps.

The secret sauce here is what Apple calls probe data. Essentially little slices of vector data that represent direction and speed transmitted back to Apple completely anonymized with no way to tie it to a specific user or even any given trip. It’s reaching in and sipping a tiny amount of data from millions of users instead, giving it a holistic, real-time picture without compromising user privacy.

If you’re driving, walking or cycling, your iPhone can already tell this. Now if it knows you’re driving it can also send relevant traffic and routing data in these anonymous slivers to improve the entire service. This only happens if your maps app has been active, say you check the map, look for directions etc. If you’re actively using your GPS for walking or driving, then the updates are more precise and can help with walking improvements like charting new pedestrian paths through parks — building out the map’s overall quality.

All of this, of course, is governed by whether you opted into location services and can be toggled off using the maps location toggle in the Privacy section of settings.

Apple says that this will have a near zero effect on battery life or data usage, because you’re already using the ‘maps’ features when any probe data is shared and it’s a fraction of what power is being drawn by those activities.

From the point cloud on up

But maps cannot live on ground truth and mobile data alone. Apple is also gathering new high resolution satellite data to combine with its ground truth data for a solid base map. It’s then layering satellite imagery on top of that to better determine foliage, pathways, sports facilities, building shapes and pathways.

After the downstream data has been cleaned up of license plates and faces, it gets run through a bunch of computer vision programming to pull out addresses, street signs and other points of interest. These are cross referenced to publicly available data like addresses held by the city and new construction of neighborhoods or roadways that comes from city planning departments.

But one of the special sauce bits that Apple is adding to the mix of mapping tools is a full on point cloud that maps the world around the mapping van in 3D. This allows them all kinds of opportunities to better understand what items are street signs (retro-reflective rectangular object about 15 feet off the ground? Probably a street sign) or stop signs or speed limit signs.

It seems like it could also enable positioning of navigation arrows in 3D space for AR navigation, but Apple declined to comment on ‘any future plans’ for such things.

Apple also uses semantic segmentation and Deep Lambertian Networks to analyze the point cloud coupled with the image data captured by the car and from high-resolution satellites in sync. This allows 3D identification of objects, signs, lanes of traffic and buildings and separation into categories that can be highlighted for easy discovery.

The coupling of high resolution image data from car and satellite, plus a 3D point cloud results in Apple now being able to produce full orthogonal reconstructions of city streets with textures in place. This is massively higher resolution and easier to see, visually. And it’s synchronized with the ‘panoramic’ images from the car, the satellite view and the raw data. These techniques are used in self driving applications because they provide a really holistic view of what’s going on around the car. But the ortho view can do even more for human viewers of the data by allowing them to ‘see’ through brush or tree cover that would normally obscure roads, buildings and addresses.

This is hugely important when it comes to the next step in Apple’s battle for supremely accurate and useful Maps: human editors.

Apple has had a team of tool builders working specifically on a toolkit that can be used by human editors to vet and parse data, street by street. The editor’s suite includes tools that allow human editors to assign specific geometries to flyover buildings (think Salesforce tower’s unique ridged dome) that allow them to be instantly recognizable. It lets editors look at real images of street signs shot by the car right next to 3D reconstructions of the scene and computer vision detection of the same signs, instantly recognizing them as accurate or not.

Another tool corrects addresses, letting an editor quickly move an address to the center of a building, determine whether they’re misplaced and shift them around. It also allows for access points to be set, making Apple Maps smarter about the ‘last 50 feet’ of your journey. You’ve made it to the building, but what street is the entrance actually on? And how do you get into the driveway? With a couple of clicks, an editor can make that permanently visible.

“When we take you to a business and that business exists, we think the precision of where we’re taking you to, from being in the right building,” says Cue. “When you look at places like San Francisco or big cities from that standpoint, you have addresses where the address name is a certain street, but really, the entrance in the building is on another street. They’ve done that because they want the better street name. Those are the kinds of things that our new Maps really is going to shine on. We’re going to make sure that we’re taking you to exactly the right place, not a place that might be really close by.”

Water, swimming pools (new to Maps entirely), sporting areas and vegetation are now more prominent and fleshed out thanks to new computer vision and satellite imagery applications. So Apple had to build editing tools for those as well.

Many hundreds of editors will be using these tools, in addition to the thousands of employees Apple already has working on maps, but the tools had to be built first, now that Apple is no longer relying on third parties to vet and correct issues.

And the team also had to build computer vision and machine learning tools that allow it to determine whether there are issues to be found at all.

Anonymous probe data from iPhones, visualized, looks like thousands of dots, ebbing and flowing across a web of streets and walkways, like a luminescent web of color. At first, chaos. Then, patterns emerge. A street opens for business, and nearby vessels pump orange blood into the new artery. A flag is triggered and an editor looks to see if a new road needs a name assigned.

A new intersection is added to the web and an editor is flagged to make sure that the left turn lanes connect correctly across the overlapping layers of directional traffic. This has the added benefit of massively improved lane guidance in the new Apple Maps.

Apple is counting on this combination of human and AI flagging to allow editors to first craft base maps and then also maintain them as the ever changing biomass wreaks havoc on roadways, addresses and the occasional park.

Here there be Helvetica

Apple’s new Maps, like many other digital maps, display vastly differently depending on scale. If you’re zoomed out, you get less detail. If you zoom in, you get more. But Apple has a team of cartographers on staff that work on more cultural, regional and artistic levels to ensure that its Maps are readable, recognizable and useful.

These teams have goals that are at once concrete and a bit out there — in the best traditions of Apple pursuits that intersect the technical with the artistic.

The maps need to be usable, but they also need to fulfill cognitive goals on cultural levels that go beyond what any given user might know they need. For instance, in the US, it is very common to have maps that have a relatively low level of detail even at a medium zoom. In Japan, however, the maps are absolutely packed with details at the same zoom, because that increased information density is what is expected by users.

This is the department of details. They’ve reconstructed replicas of hundreds of actual road signs to make sure that the shield on your navigation screen matches the one you’re seeing on the highway road sign. When it comes to public transport, Apple licensed all of the type faces that you see on your favorite subway systems, like Helvetica for NYC. And the line numbers are in the exact same order that you’re going to see them on the platform signs.

It’s all about reducing the cognitive load that it takes to translate the physical world you have to navigate through into the digital world represented by Maps.

Bottom line

The new version of Apple Maps will be in preview next week with just the Bay Area of California going live. It will be stitched seamlessly into the ‘current’ version of Maps, but the difference in quality level should be immediately visible based on what I’ve seen so far.

Better road networks, more pedestrian information, sports areas like baseball diamonds and basketball courts, more land cover including grass and trees represented on the map as well as buildings, building shapes and sizes that are more accurate. A map that feels more like the real world you’re actually traveling through.

Search is also being revamped to make sure that you get more relevant results (on the correct continents) than ever before. Navigation, especially pedestrian guidance, also gets a big boost. Parking areas and building details to get you the last few feet to your destination are included as well.

What you won’t see, for now, is a full visual redesign.

“You’re not going to see huge design changes on the maps,” says Cue. “We don’t want to combine those two things at the same time because it would cause a lot of confusion.”

Apple Maps is getting the long awaited attention it really deserves. By taking ownership of the project fully, Apple is committing itself to actually creating the map that users expected of it from the beginning. It’s been a lingering shadow on iPhones, especially, where alternatives like Google Maps have offered more robust feature sets that are so easy to compare against the native app but impossible to access at the deep system level.

The argument has been made ad nauseam, but it’s worth saying again that if Apple thinks that mapping is important enough to own, it should own it. And that’s what it’s trying to do now.

“We don’t think there’s anybody doing this level of work that we’re doing,” adds Cue. “We haven’t announced this. We haven’t told anybody about this. It’s one of those things that we’ve been able to keep pretty much a secret. Nobody really knows about it. We’re excited to get it out there. Over the next year, we’ll be rolling it out, section by section in the US.”

Source: Mobile – Techcruch

FB Messenger auto-translation chips at US/Mexico language wall

FB Messenger auto-translation chips at US/Mexico language wall

Facebook’s been criticized for tearing America apart, but now it will try to help us forge bonds with our neighbors to the south. Facebook Messenger will now offer optional auto-translation of English to Spanish and vice-versa for all users in the United States and Mexico. It’s a timely launch given the family separation troubles at the nation’s border.

The feature could facilitate cross-border and cross-language friendships, business and discussion that might show people in the two countries that deep down we’re all just human. It could be especially powerful for U.S. companies looking to use Messenger for conversational commerce without having to self-translate everything.

Facebook tells me “we were pleased with the results” following a test using AI to translate the language pair in Messenger for U.S. Facebook Marketplace users in April.

Now when users receive a message that is different from their default language, Messenger’s AI assistant M will ask if they want it translated. All future messages in that thread will be auto-translated unless a user turns it off. Facebook plans to bring the feature to more language pairs and countries soon.

A Facebook spokesperson tells me, “The goal with this launch is really to enable people to communicate with people they wouldn’t have been able to otherwise, in a way that is natural and seamless.”

Starting in 2011, Facebook began offering translation technology for News Feed posts and comments. For years it relied on Microsoft Bing’s translation technology, but Facebook switched to its own stack in mid-2016. By then it was translating 2 billion pieces of text a day for 800 million users.

Conversational translation is a lot tougher than social media posts, though. When we chat with friends, it’s more colloquial and full of slang. We’re also usually typing in more of a hurry and can be less accurate. But if Facebook can reliably figure out what we’re saying, Messenger could become the modern-day Babel Fish. At 2016’s F8, Facebook CEO Mark Zuckerberg threw shade on Donald Trump saying, “instead of building walls, we can build bridges.” Trump still doesn’t have that wall, and now Zuck is building a bridge with technology.

Source: Mobile – Techcruch

New system connects your mind to a machine to help stop mistakes

New system connects your mind to a machine to help stop mistakes
How do you tell your robot not do something that could be catastrophic? You could give it a verbal or programmatic command or you could have it watch your brain for signs of distress and have it stop itself. That’s what researchers at MIT’s robotics research lab have done with a system that is wired to your brain and tells robots how to do their job.
The initial system is fairly simple. A scalp EEG and EMG system is connected to a Baxter work robot and lets a human wave or gesture when the robot is doing something that it shouldn’t be doing. For example, the robot could regularly do a task – drilling holes, for example – but when it approaches an unfamiliar scenario the human can gesture at the task that should be done.
“By looking at both muscle and brain signals, we can start to pick up on a person’s natural gestures along with their snap decisions about whether something is going wrong,” said PhD candidate Joseph DelPreto. “This helps make communicating with a robot more like communicating with another person.”
Because the system uses nuances like gestures and emotional reactions you can train robots to interact with humans with disabilities and even prevent accidents by catching concern or alarm before it is communicated verbally. This lets workers stop a robot before it damages something and even help the robot understand slight changes to its tasks before it begins.
In their tests the team trained Baxter to drill holes in an airplane fuselage. The task changed occasionally and a human standing nearby was able to gesture to the robot to change position before it drilled, essentially training it to do new tasks in the midst of its current task. Further, there was no actual programming involved on the human’s part, just a suggestion that the robot move the drill left or right on the fuselage. The most important thing? Humans don’t have to think in a special way or train themselves to interact with the machine.
“What’s great about this approach is that there’s no need to train users to think in a prescribed way,” said DelPreto. “The machine adapts to you, and not the other way around.”
The team will present their findings at the Robotics: Science and Systems (RSS) conference.

Source: Gadgets – techcrunch

Google brings offline neural machine translations for 59 languages to its Translate app

Google brings offline neural machine translations for 59 languages to its Translate app

Currently, when the Google Translate apps for iOS and Android has access to the internet, its translations are far superior to those it produces when it’s offline. That’s because the offline translations are phrase-based, meaning they use an older machine translation technique than the machine learning-powered systems in the cloud that the app has access to when it’s online. But that’s changing today. Google is now rolling out offline Neural Machine Translation (NMT) support for 59 languages in the Translate apps.

Today, only a small number of users will see the updated offline translations, but it will roll out to all users within the next few weeks.

The list of supported languages consists of a wide range of languages. Because I don’t want to play favorites, here is the full list: Afrikaans, Albanian, Arabic, Belarusian, Bengali, Bulgarian, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian, Creole, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Irish, Italian, Japanese, Jannada, Korean, Latvian, Lithuanian, Macedonian, Malay, Maltese, Marathi, Norwegian, Persian, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Vietnamese and Welsh.

In the past, running these deep learning models on a mobile device wasn’t really an option since mobile phones didn’t have the right hardware to efficiently run them. Now, thanks to both advances in hardware and software, that’s less of an issue and Google, Microsoft and others have also found ways to compress these models to a manageable size. In Google’s case, that’s about 30 to 40 megabytes per language.

It’s worth noting that Microsoft also announced a similar feature for its Translator app earlier this year. It uses a very similar technique, but for the time being, it only supports about a dozen languages.

Source: Mobile – Techcruch

Apple introduces the AI phone

Apple introduces the AI phone

At Apple’s WWDC 2018 — an event some said would be boring this year with its software-only focus and lack of new MacBooks and iPads — the company announced what may be its most important operating system update to date with the introduction of iOS 12. Through a series of Siri enhancements and features, Apple is turning its iPhone into a highly personalized device, powered by its Siri AI.

This “new AI iPhone” — which, to be clear, is your same ol’ iPhone running a new mobile OS — will understand where you are, what you’re doing and what you need to know right then and there.

The question now is will users embrace the usefulness of Siri’s forthcoming smarts, or will they find its sudden insights creepy and invasive?

Siri Suggestions

After the installation of iOS 12, Siri’s Suggestions will be everywhere.

In the same place on the iPhone Search screen where you today see those Siri suggested apps to launch, you’ll begin to see other things Siri thinks you may need to know, too.

For example, Siri may suggest that you:

  • Call your grandma for her birthday.
  • Tell someone you’re running late to the meeting via a text.
  • Start your workout playlist because you’re at the gym.
  • Turn your phone to Do Not Disturb at the movies.

And so on.

These will be useful in some cases, and perhaps annoying in others. (It would be great if you could swipe on the suggestions to further train the system to not show certain ones again. After all, not all your contacts deserve a birthday phone call.)

Siri Suggestions will also appear on the Lock Screen when it thinks it can help you perform an action of some kind. For example, placing your morning coffee order — something you regularly do around a particular time of day — or launching your preferred workout app, because you’ve arrived at the gym.

These suggestions even show up on Apple Watch’s Siri watch face screen.

Apple says the relevance of its suggestions will improve over time, based on how you engage.

If you don’t take an action by tapping on these items, they’ll move down on the watch face’s list of suggestions, for instance.

AI-powered workflows

These improvements to Siri would have been enough for iOS 12, but Apple went even further.

The company also showed off a new app called Siri Shortcuts.

The app is based on technology Apple acquired from Workflow, a clever — if somewhat advanced — task automation app that allows iOS users to combine actions into routines that can be launched with just a tap. Now, thanks to the Siri Shortcuts app, those routines can be launched by voice.

Onstage at the developer event, the app was demoed by Kim Beverett from the Siri Shortcuts team, who showed off a “heading home” shortcut she had built.

When she tells Siri she’s “heading home,” her iPhone simultaneously launched directions for her commute in Apple Maps, set her home thermostat to 70 degrees, turned on her fan, messaged an ETA to her roommate and launched her favorite NPR station.

That’s arguably very cool — and it got a big cheer from the technically minded developer crowd — but it’s most certainly a power user feature. Launching an app to build custom workflows is not something everyday iPhone users will do right off the bat — or in some cases, ever.

Developers to push users to Siri

But even if users hide away this new app in their Apple “junk” folder, or toggle off all the Siri Suggestions in Settings, they won’t be able to entirely escape Siri’s presence in iOS 12 and going forward.

That’s because Apple also launched new developer tools that will allow app creators to build directly into their own apps integrations with Siri.

Developers will update their apps’ code so that every time a user takes a particular action — for example, placing their coffee order, streaming a favorite podcast, starting their evening jog with a running app or anything else — the app will let Siri know. Over time, Siri will learn users’ routines — like, on many weekday mornings, around 8 to 8:30 AM, the user places a particular coffee order through a coffee shop app’s order ahead system.

These will inform those Siri Suggestions that appear all over your iPhone, but developers will also be able to just directly prod the user to add this routine to Siri right in their own apps.

In your favorite apps, you’ll start seeing an “Add to Siri” link or button in various places — like when you perform a particular action — such as looking for your keys in Tile’s app, viewing travel plans in Kayak, ordering groceries with Instacart and so on.

Many people will probably tap this button out of curiosity — after all, most don’t watch and rewatch the WWDC keynote like the tech crowd does.

The “Add to Siri” screen will then pop up, offering a suggestion of voice prompt that can be used as your personalized phase for talking to Siri about this task.

In the coffee ordering example, you might be prompted to try the phrase “coffee time.” In the Kayak example, it could be “travel plans.”

You record this phrase with the big, red record button at the bottom of the screen. When finished, you have a custom Siri shortcut.

You don’t have to use the suggested phrase the developer has written. The screen explains you can make up your own phrase instead.

In addition to being able to “use” apps via Siri voice commands, Siri can also talk back after the initial request.

It can confirm your request has been acted upon — for example, Siri may respond, “OK. Ordering. Your coffee will be ready in 5 minutes,” after you said “Coffee time” or whatever your trigger phrase was.

Or it can tell you if something didn’t work — maybe the restaurant is out of a food item on the order you placed — and help you figure out what to do next (like continue your order in the iOS app).

It can even introduce some personality as it responds. In the demo, Tile’s app jokes back that it hopes your missing keys aren’t “under a couch cushion.”

There are a number of things you could do beyond these limited examples — the App Store has more than 2 million apps whose developers can hook into Siri.

And you don’t have to ask Siri only on your phone — you can talk to Siri on your Apple Watch and HomePod, too.

Yes, this will all rely on developer adoption, but it seems Apple has figured out how to give developers a nudge.

Siri Suggestions are the new Notifications

You see, as Siri’s smart suggestions spin up, traditional notifications will wind down.

In iOS 12, Siri will take note of your behavior around notifications, and then push you to turn off those with which you don’t engage, or move them into a new silent mode Apple calls “Delivered Quietly.” This middle ground for notifications will allow apps to send their updates to the Notification Center, but not the Lock Screen. They also can’t buzz your phone or wrist.

At the same time, iOS 12’s new set of digital well-being features will hide notifications from users at particular times — like when you’ve enabled Do Not Disturb at Bedtime, for example. This mode will not allow notifications to display when you check your phone at night or first thing upon waking.

Combined, these changes will encourage more developers to adopt the Siri integrations, because they’ll be losing a touchpoint with their users as their ability to grab attention through notifications fades.

Machine learning in photos

AI will further infiltrate other parts of the iPhone, too, in iOS 12.

A new “For You” tab in the Photos app will prompt users to share photos taken with other people, thanks to facial recognition and machine learning.  And those people, upon receiving your photos, will then be prompted to share their own back with you.

The tab will also pull out your best photos and feature them, and prompt you to try different lighting and photo effects. A smart search feature will make suggestions and allow you to pull up photos from specific places or events.

Smart or creepy?

Overall, iOS 12’s AI-powered features will make Apple’s devices more personalized to you, but they could also rub some people the wrong way.

Maybe people won’t want their habits noticed by their iPhone, and will find Siri prompts annoying — or, at worst, creepy, because they don’t understand how Siri knows these things about them.

Apple is banking hard on the fact that it’s earned users’ trust through its stance on data privacy over the years.

And while not everyone knows that Siri is does a lot of its processing on your device, not in the cloud, many do seem to understand that Apple doesn’t sell user data to advertisers to make money.

That could help sell this new “AI phone” concept to consumers, and pave the way for more advancements later on.

But on the flip side, if Siri Suggestions become overbearing or get things wrong too often, it could lead users to just switch them off entirely through iOS Settings. And with that, Apple’s big chance to dominate in the AI-powered device market, too.

Source: Mobile – Techcruch

Watch a hard-working robot improvise to climb drawers and cross gaps

Watch a hard-working robot improvise to climb drawers and cross gaps
A robot’s got to know its limitations. But that doesn’t mean it has to accept them. This one in particular uses tools to expand its capabilities, commandeering nearby items to construct ramps and bridges. It’s satisfying to watch but, of course, also a little worrying.
This research, from Cornell and the University of Pennsylvania, is essentially about making a robot take stock of its surroundings and recognize something it can use to accomplish a task that it knows it can’t do on its own. It’s actually more like a team of robots, since the parts can detach from one another and accomplish things on their own. But you didn’t come here to debate the multiplicity or unity of modular robotic systems! That’s for the folks at the IEEE International Conference on Robotics and Automation, where this paper was presented (and Spectrum got the first look).
SMORES-EP is the robot in play here, and the researchers have given it a specific breadth of knowledge. It knows how to navigate its environment, but also how to inspect it with its little mast-cam and from that inspection derive meaningful data like whether an object can be rolled over, or a gap can be crossed.
It also knows how to interact with certain objects, and what they do; for instance, it can use its built-in magnets to pull open a drawer, and it knows that a ramp can be used to roll up to an object of a given height or lower.
A high-level planning system directs the robots/robot-parts based on knowledge that isn’t critical for any single part to know. For example, given the instruction to find out what’s in a drawer, the planner understands that to accomplish that, the drawer needs to be open; for it to be open, a magnet-bot will have to attach to it from this or that angle, and so on. And if something else is necessary, for example a ramp, it will direct that to be placed as well.
The experiment shown in this video has the robot system demonstrating how this could work in a situation where the robot must accomplish a high-level task using this limited but surprisingly complex body of knowledge.

In the video, the robot is told to check the drawers for certain objects. In the first drawer, the target objects aren’t present, so it must inspect the next one up. But it’s too high — so it needs to get on top of the first drawer, which luckily for the robot is full of books and constitutes a ledge. The planner sees that a ramp block is nearby and orders it to be put in place, and then part of the robot detaches to climb up and open the drawer, while the other part maneuvers into place to check the contents. Target found!
In the next task, it must cross a gap between two desks. Fortunately, someone left the parts of a bridge just lying around. The robot puts the bridge together, places it in position after checking the scene, and sends its forward half rolling towards the goal.
These cases may seem rather staged, but this isn’t about the robot itself and its ability to tell what would make a good bridge. That comes later. The idea is to create systems that logically approach real-world situations based on real-world data and solve them using real-world objects. Being able to construct a bridge from scratch is nice, but unless you know what a bridge is for, when and how it should be applied, where it should be carried and how to get over it, and so on, it’s just a part in search of a whole.
Likewise, many a robot with a perfectly good drawer-pulling hand will have no idea that you need to open a drawer before you can tell what’s in it, or that maybe you should check other drawers if the first doesn’t have what you’re looking for!
Such basic problem-solving is something we take for granted, but nothing can be taken for granted when it comes to robot brains. Even in the experiment described above, the robot failed multiple times for multiple reasons while attempting to accomplish its goals. That’s okay — we all have a little room to improve.

Source: Gadgets – techcrunch

20 takeaways from Meeker’s 294-slide Internet Trends report

20 takeaways from Meeker’s 294-slide Internet Trends report

This is a must-read for understanding the tech industry. We’ve distilled famous investor Mary Meeker’s annual Internet Trends report down from its massive 294 slides of stats and charts to just the most important insights. Click or scroll through to learn what’s up with internet growth, screen addiction, e-commerce, Amazon versus Alibaba, tech investment and artificial intelligence.

Source: Mobile – Techcruch

Snips announces an ICO and its own voice assistant device

Snips announces an ICO and its own voice assistant device
French startup Snips has been working on voice assistant technology that respects your privacy. And the company is going to use its own voice assistant for a set of consumer devices. As part of this consumer push, the company is also announcing an initial coin offering.
Yes, it sounds a bit like Snips is playing a game of buzzword bingo. Anyone can currently download the open source Snips SDK and play with it with a Raspberry Pi, a microphone and a speaker. It’s private by design, you can even make it work without any internet connection. Companies can partner with Snips to embed a voice assistant in their own devices too.
But Snips is adding a B2C element to its business. This time, the company is going to compete directly with Amazon Echo and Google Home speakers. You’ll be able to buy the Snips AIR Base and Snips AIR Satellites.
The base will be a good old smart speaker, while satellites will be tiny portable speakers that you can put in all your rooms. The company plans to launch those devices in 18 months.

By default, Snips devices will come with basic skills to control your smart home devices, get the weather, control music, timers, alarms, calendars and reminders. Unlike the Amazon Echo or Google Home, voice commands won’t be sent to Google’s or Amazon’s servers.
Developers will be able to create skills and publish them on a marketplace. That marketplace will run on a new blockchain — the AIR blockchain.
And that’s where the ICO comes along. The marketplace will accept AIR tokens to buy more skills. You’ll also be able to generate training data for voice commands using AIR tokens. To be honest, I’m not sure why good old credit card transactions weren’t enough. But I guess that’s a good way to raise money.

Source: Gadgets – techcrunch