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What is the new Apple U1 chip, and why is it important?

What is the Apple U1 chip, and why is it important.The biggest Apple announcement today was what Apple actually didn’t announce—yet.“Hey Siri, we lost Spot the dog, do you know where he is?”Siri:”Spot is located 87 feet forward and down 2 feet from the height of the iPhone. Please hold up your phone and follow the Balloon to Spot’s location”Today, September 10th, 2019 Apple announced the iPhone 11 and iPhone 11 Pro series of phones. Not mentioned on the stage, but briefly shown on the screen during Phil Schiller’s presentation was the new Apple U1 chip. Hidden in plain sight much like how he pre-announced Apple Pay, we see the Apple U1 Chip there for the world to see, yet most missed it until after the event.Specimen Apple Event September 10th, 2019 showing Apple U1 Chip.In 2012 Phil did precisely the same thing during the announcement of TouchID as a credit card machine, a Hypercom device [0], was presented on the screen as a potential use case for TouchID. I wrote this would be a nearly 100% confirmation of what became Apple Pay (I called it the iWallet, I know very 2012 of me). Many folks in the payment industry including disruptive startups thought me insane and went about becoming redundant when Apple Pay was released. Of course I had far more basis than a single Phil image. History is about to repeat itself.Specimen of Phil showing TouchID use cases in 2012.Why was the Apple U1 chip on a graphic behind Phil and not announced overtly and only lightly mentioned on the Apple website? We will explore this in more detail at the end. Yet the first mention of how Apple will use the U1 Chip was presented on Apple’s own website as a new highly directional version of AirDrop.The text on Apple’s website for iPhone 11 series says it all:Ultra Wideband technology comes to iPhone.The new Apple‑designed U1 chip uses Ultra Wideband technology for spatial awareness — allowing iPhone 11 to precisely locate other U1‑equipped Apple devices. Think GPS at the scale of your living room. So if you want to share a file with someone using AirDrop, just point your iPhone at theirs and they’ll be first on the list.And:Can you be more precise? Yes.The new Apple‑designed U1 chip uses Ultra Wideband technology for spatial awareness — allowing iPhone 11 Pro to precisely locate other U1‑equipped Apple devices. It’s like adding another sense to iPhone, and it’s going to lead to amazing new capabilities.With U1 and iOS 13, you can point your iPhone toward someone else’s, and AirDrop will prioritize that device so you can share files faster.And that’s just the beginning.Just the beginning, indeed.Specimen Apple website September 10th, 2019 showing Apple U1 Chip promotion.Specimen Apple website September 10th, 2019 showing Apple U1 Chip promotion.Meet Ultra-Wide Band Radio TechnologyThe “U” in the U1 chip relates to the Ultra-Wide Band Radio Technology (UWB) [1] technology it uses. UWB can be used for many application and use cases. One use case that will become very large for Apple as they move to AR/MR technology and Apple Glasses is to be able to track spatial relationships of objects. One way to do this is using lasers and IR systems, and Apple is already doing this to some degree with FaceID and Animoji. The other way to do this is via the radio spectrum.The Apple U1 Chip most assuredly uses a variant of the IEEE 802.15 WPAN from the IEEE 802.15.4z Enhanced Impulse Radio group of which Apple is an active member. IEEE 802.15.4z to put in simple terms wants to absorb, in some ways, and extend Bluetooth, NFC, WiFi and other network standards and protocols.The early concept of this technology was used in an all but abandoned Apple initiative called iBeacons [2]. This technology was centered around Bluetooth and Bluetooth Low Energy (BLE). The idea was sound, however the technology was low resolution, so low that it would be hard to be with-in a few feet without triangularization of 3 or more iBeacons and even then it can drift significantly with heat and obstacle issues.Parallel to the iBeacon research, Apple was testing a newer and more exacting technology in their research labs in 2005. By 2006, before the iPhone was even announced, they applied for a patent for “Ultra-wideband radios for time-of-flight-ranging and network position estimation” via a research grant at Livermore Labs. It took until September 2010 for the patent application to be released by the US Patent and Trademark Office. Apple went on to do a lot of work with iBeacons and BLE. However, the 3 dimensional spatial resolution was not nearly as accurate as Apple needed and thus they abandoned the concept of the iBeacon.The “Smoking Gun” Apple UWB PatentsMore recently Apple has applied for a few more patents centered around UWB. Inventor Joachim S. Hammerschmidt has developed some amazing extension of this technology. Apple inventor Benjamin Vigier has also contributed greatly to the UWB beacon concept. Joachim is a bit of an Apple patent savant submitting a few dozen patents on UWB and other radio frequency technologies.Specimen Joachim S. Hammerschmidt Apple Patents.Even though the concepts of UWB has been around since the dawn of radio first developed in a useable way by RCA in the 1950s, the miniaturization and low power chips has it taken on this new form.I have surfaced three very interesting Apple patents centered around UWB that most have overlooked.Beacon Triggered Processes 2019 (United States Patent Application: 0190272567)Ultra-wideband radios for time-of-flight-ranging and network position estimation 2006 (United States Patent Application: 0100225541)TIME INSTANT REFERENCE FOR ULTRA WIDEBAND SYSTEMS 2018 (United States Patent Application: 0190199398)Pulse Shaping Interoperability Protocol for Ultra Wideband Systems 2017 (United States Patent Application: 0190007093)Specimen Joachim S. Hammerschmidt Apple Patent embodiment.Clearly Apple had predicted the need for UWB as far back as the early 2000s. There are more Apple patents that relate to this technology, but these give an interesting overview:Beacon Triggered ProcessesAbstractTechniques and systems for beacon triggered processes are disclosed. A described technique includes causing a beacon device to broadcast a beacon message, the beacon device being within the vicinity of an establishment; detecting a presence of a user of a mobile device based on receiving from the mobile device a first message that is responsive to the beacon message; retrieving a transaction record based on a user or mobile device identifier in the first message; generating and transmitting a second message based on the transaction record to facilitate a completion of a transaction associated with the transaction record at the establishment; the second message being configured to provide notification of an arrival of the user and dispatch an employee to meet the user and handle the transaction; and generating and transmitting a third message based on the transaction record to facilitate the completion of the transaction at the mobileSpecimen Benjamin Vigier Apple Patent embodiment.And:Ultra-wideband radios for time-of-flight-ranging and network position estimationAbstractThis invention provides a novel high-accuracy indoor ranging device that uses ultra-wideband (UWB) RF pulsing with low-power and low-cost electronics. A unique of the present invention is that it exploits multiple measurements in time and space for very accurate ranging. The wideband radio signals utilized herein are particularly suited to ranging in harsh RF environments because they allow signal reconstruction in spite of multipath propagation distortion. Furthermore, the ranging and positioning techniques discussed herein directly address many of the known technical challenges encountered in UWB localization regarding synchronization and sampling. In the method developed, noisy, corrupted signals can be recovered by repeating range measurements across a channel, and the distance measurements are combined from many locations surrounding the target in a way that minimizes the range biases associated to indirect flight paths and through-wall propagation delays.And:TIME INSTANT REFERENCE FOR ULTRA WIDEBAND SYSTEMSAbstractEmbodiments enable communicating Ultra Wideband (UWB) devices to collaborate by exchanging pulse shape information. The UWB devices use the pulse shape information to improve ranging accuracy. The improved ranging accuracy can be used in complex multipath environments where advanced estimation schemes are used to extract an arriving path for time-of-flight estimation. To determine the pulse shape information to be shared, some embodiments include determining location information of a UWB device and selecting the pulse shape information that satisfies regional aspects. The pulse shape information includes a time-zero index specific to a ranging signal that is used by UWB receivers to establish timestamps time-of-flight calculations. Some embodiments include measuring performance characteristics and selecting different pulse shape information based on the performance characteristics for improved accuracy.And:Pulse Shaping Interoperability Protocol for Ultra Wideband SystemsAbstractEmbodiments enable communicating Ultra Wideband (UWB) devices to collaborate by exchanging pulse shape information. The UWB devices use the pulse shape information to improve ranging accuracy. The improved ranging accuracy can be used in complex multipath environments where advanced estimation schemes are used to extract an arriving path for time-of-flight estimation. To determine the pulse shape information to be shared, some embodiments include determining location information of a UWB device and selecting the pulse shape information that satisfies regional aspects. The pulse shape information includes a time-zero index specific to a ranging signal that is used by UWB receivers to establish timestamps time-of-flight calculations. Some embodiments include measuring performance characteristics and selecting different pulse shape information based on the performance characteristics for improved accuracy.In the “Pulse Shaping Interoperability Protocol for Ultra Wideband Systems” Apple patent we find very enlighten embodiments:[0021] Precise knowledge of pulse shape information used at a station's transmitter allows the use of receivers that isolate pulse shaping or other filtering effects from true propagation channel effects. Knowledge of the pulse shape information also allows the use of signal processing techniques that may be referred to as "deconvolution" techniques--methods to look at an overall received signal (e.g., end-to-end impulse response from transmitter to receiver) and factor out known artifacts such as, for example, transmitter pulse shaping including antenna effects or receiver transfer characteristics. These signal processing techniques allow extraction of a desired contribution of a wireless propagation channel in the overall system response; in turn, this extraction can be used to determine a time instant of an arriving propagation path.Other embodiments present an example system that may include but is not limited to UWB devices such as wireless communication devices (iPhones # 110 and 120), vehicular transponder device (#130), entry transponder device for doors (#140), a household device (#150 thermostat), pet leash tag (#160), and anchor nodes l70a­l70c.I have been studying patents for over 35 years and in particular Apple patents. The Apple UWB patents have been of great interest to me as I knew UWB will become not only an indoor mapping system, like GPS for indoor spaces, it will become crucial to AR/MR/VR environments for fine tuning of spatial coordinates. UWB will also be very useful with automobiles, drones, and robotic systems. I wrote a few reports for clients and one VC commented that “this would forever change the way we view indoor spaces”, I agree.These Apple patents are a potpourri of ways Apple can and very likely will use UWB. I could literally write a book on how this will play out for Apple just via these patents. Some of what I have learned since 2010 I will use in this answer.The Apple UWB Personal Radar SystemUWB can also serve as a sort of personal radar that can self reference the waves it sends out and echo locate your spacial world with a high degree of precision. I can see this as one way to avert some folks hunched over and thumb clawing at the screen walking down the street and the iPhone puts up a notification of an imminent collision.Specimen of a whimsical radar screen.Although humorous, if Apple makes this into an open standard, and there is evidence some aspects may become open sourced, imagine a world where UWBs are in all automobiles and pedestrians. The collision detection and avoidance systems can become very powerful, and save many lives.How does UWB Work?UWB IEEE 802.15 WPAN devices collaborate with each other by exchanging pulsed shapes of information that can be used for a future ranging exchange. This is achieved by the receiving UWB devices using the pulse shape information to improve the ranging accuracy. The improved ranging accuracy can be used in complex multi-path environments where advanced estimation schemes are used to extract an arriving path for time-of-flight estimation. Time-of-flight is the basis of how UWB works, very much like GPS.Specimen of waveforms used to calculate time of flight.The pulse shape information includes a time-zero index specific to a received ranging signal that is used by UWB receivers to establish timestamps for time-of-flight calculations.This includes measuring performance characteristics and selecting different pulse shape information.The UWB receiving pulse shape information from other devices, where the pulse shape information is used in UWB communications between the electronic device and the another electronic device, receiving a ranging signal that uses first pulse shape information, and determining a distance between the electronic device and the another electronic device based at least in part on the pulse shape information and the ranging signal.Thus determining the distance includes calculating a time-of-flight associated with the ranging signal and the pulse shape information includes a time-zero index that may be a sample of a main lobe of the pulse shape information (e.g., a first sample or a center sample of a main lobe of the pulse shape information.) The pulse shape information also satisfies one or more regional aspects associated with location information of the electronic device.One or more of the anchor nodes may be used in conjunction with an iPhone or other device to improve the accuracy and reliability of ranging. The devices may triangulate and determine a geographic location that may be used to provide local direction information.The primary UWB can also serve as its own Anchor Node and self reference the ranging signal pattern itself. Very much like personal radar systems, this may not have the same high resolution as using two or more devices to triangulate, but it can be quite useful.Apple U1 ChipThe Apple U1 chip is a application specific low power chip design very much like the new Decawave impulse radio ultra-wideband (IR-UWB) DW1000 Radio IC chip set. They have sold millions of these chips thus far and has better than 10 centimeters guaranteed indoor accuracy. It is very possible the Apple U1 chip uses the Decawave chip, licensed technology or a customized OEM version. It is possible to achieve better than 3 centimeters of accuracy in theory with this technology.Specimen Decawave DW1000 chip.The Decawave DW1000 Radio IC [3] for example, can move 6.8Mbps of data with an accuracy that is 100x better than WiFi or Bluetooth. It can reach 290 meters of distance with a very minimal power requirement with a 50x faster speed compared to standard GPS latency. Although the Apple U1 chip is not yet released, I suspect we will expect the same or better specifications. Thus based on the DW1000 as the base, we can imagine just how important the Apple U1 will become.Specimen Apple U1 Chip.Although Apple only mentioned indirectly the U1 chip in the new iPhone 11 series, I think it is likely to be a part of Apple Watch Series 5. And if not yet in Apple Watch Series 5, it will be in a future version. I also imagine the U1 in AirPods, AirPod cases, Apple Glasses, MacBook Pros and of course in a stand alone device similar to the Tile.Specimen Apple U1 Chip.The very low battery consumption of the U1 chip may make it possible for a single coin cell hearing aid battery to have a life of a year or more in normal settings. It is also possible to use the ambient radio frequencies that surround all of us to charge the battery using the patented technology from the Apple acquired company Passif [4]. It seems a very natural use case for Passif technology and was once used to power early internal testing of iBeacons.Specimen of UWB topography.Using the very low power consumption of the Apple U1 Chip and Passif ambient radio wave battery charging, it seems that we may see a less than 2 inch disc, what I will call this Locate or AppleLocate (internally called Rose Tag by Apple), device in the market. It seems the existence of AppleLocate was revealed in iPhone Find My app. Thus it seems very likely one of the first things we see utilizing the U1 chip, outside of the iPhone 11 series is this new device. Many have speculated this would be a stand alone device and today this seems to be the case. I have asserted this technology would come about since 2012 originally as iBeacons and later as UWB.Specimen of the  Locate or AppleLocate image found in Apple software.The AppleLocate tags will be relatively inexpensive, starting at about $20 and likely drifting to less than $2 in high production, it seems likely it will become widely used for countless reasons.We will thus see AppleLocate tags in just about all Apple devices at some point for very precise location tracking and perimeter fencing. For example, you can be notified when the AppleLocate tag has entered or left an area as well as the U! chip built into other Apple devices. It will become orders of magnitude more difficult to steal an Apple product in the future with perimeter fencing breech notifications.HyperLocal And HyperPrivateIt is entirely possible to build a useful AR/MR/VR map of any indoor space using the Apple U1 chip in just a few minutes few minutes. This can be utilized with the same laser and/or IR technology found in the iPhone for FaceID. Thus with a combinations of the Apple U1 chip and Apple A13 Bionic neural engine we will have one of the most sophisticated spacial mapping and analyzing systems in any currently available consumer device.Indoor mapping has been tried via many methods over the years, including using the Roomba robotic vacuum cleaner systems. Roomba met with a great deal of push back by users of their products when it was discovered the company may be selling indoor maps of the user’s homes [5] using SLAM technology. In the case of Apple, the U1 chip along with the results of FaceID/TouchID system is stored in the Secure Enclave. The data stored in the Secure Enclave is physically, nearly impossible to retrieve from the chip and is held only local to the device fully encrypted.There will be endless allegations that Apple is trying to collect and sell the results of indoor mapping and other telemetries from the Apple U1 Chip, but this will not be the case. Apple simply does not have access to the information and could not use it without you supplying it.Developer OpportunityIt seems very likely Apple will open up some of the abilities of the Apple U1 chip to developers. This will of course be by explicit permissions granted to the developer much like sharing location data. However I feel strongly Apple will never let a free flow of U! chip data be shared with any developer app. The opportunities with a well crafted API supply by Apple can be quite amazing. From AR use case to collision avoidance systems and just about everything in-between, this will become a very interesting new frontier for Apple developers. I think we will begin to see the API by the Apple World Wide Developer Conference 2020.Apple Glasses: The Held And Worn New Software VersionMore than anything else, the Apple U1 Chip helps telegraph the Apple Glasses AR/MR platform. Internally at Apple this is known under a few names, Project StarBoard is linked to the first generations of Apple Glasses. The ARDisplayDevice SDK in iPhoneOS 13 also clearly confirms an external AR/MR display device. The first versions will use the iPhone 11 Series as a tether base—wirelessly. Much like Apple Watch and Apple CarPlay the Apple Glasses will interact and interplay with full apps on the iPhone. There is likely to be an Apple U1 Chip built-in to the Apple Glasses as well as new Siri chips and Bone Conduction sound chip Apple has been working on. The Apple U1 Chip on the Apple Glasses and on the iPhone will work together as node anchors.Specimen of a hypothetical Apple Glasses display monitor using AirPlay.Apple will allow for two versions of the same code to run. Called held as in the iPhone and worn as in Apple glasses. And like CarPlay the worn version for the first generations of Apple glasses will have less information. Apple has slowly been adding features that will use Apple Glasses in Apple Maps, Find My app and other Apple apps to help build the infrastructure and early developer interest in Apple Glasses. Thus as we see use cases slowly rolled out by Apple for the Apple U1 Chip, imagine how it will related to Apple Glasses. As this slowly plays out, it will become abundantly clear how deeply Apple has been thinking about this.The Apple U1 chip will be used to help decode hand gestures in an AR/MR/VR spacial environment. The unfortunate aspect is just like the QWERTY keyboard there may be dozens of “standards”. It is my sincere hope that American Sign Language becomes the “Silent Voice” for this new user interface. It is robust and nuanced and will include a far wide audience. If we must learn new gestures, let me be gestures that a significant portion of the population already know. A great artifact is compared to thumb clawing at glass screens on the 1870s QWERTY keyboard, ASL is 10X faster. The Apple U1 Chip will help bring this about with the combination of other technologies.If we gave an #ASLVoice to this next generation adopting American Sign Language to spacial gestures we would unlock future generations from thumb clawing on glass screens into the next century.It is a #VoiceFirst future, #ASLVoice will change the world in astonishing ways.— Brian Roemmele (@BrianRoemmele) September 13, 2019Specimen of open source MediaPipe GPU based software decoding ASL.I have been experimenting with Glasses in my garage lab for a few years that display contextual information and interact via a Voice First system I call The Intelligence Amplifier. I use a number of systems in my cheap glasses including the Decawave DW1000 Radio IC with a very early version of the IEEE 802.15.4z spec. I have had robust success and see no reason why Apple will not do as robustly with this technology. There is no doubt Apple Glasses will be a Voice First device.I‘ve been using a UWB IEEE 802.15.4z WPAN chip from Decawave the IR-UWB DW1000 for over a year with robust success.I can now say I have this chip in my cheap glasses below.Many may find interesting, the Apple U1 Chip is IEEE 802.15.4z and based on the DW1000.Coincidence 🧐— Brian Roemmele (@BrianRoemmele) September 12, 2019Specimen of my cheap glasses modifed in my garage lab..Apple Pay, Retail and Industrial UsesAs I mentioned, there will be countless new use cases for the Apple U1 Chip. I built the first and still the largest Apple Pay Map in the world, PayFinders [6]. One of my challenges was to push a notification to the iPhone user’s phone when they were inside the business, but also close to the checkout. In large stores like Target, I had great accuracy. However In smaller stores the boundaries were in and sometimes near the store. I urged Apple to use Bluetooth at top line merchants to help users know where an Apple Pay Credit Card machine was located and operational. My research showed people just did not want to ask or even test in most circumstances. With the AppleLocate tags on Credit Card Machines, the Apple Pay user can be directed precisely to with-in a few millimeters.The same can be said of product locations. The “Beacon Triggered Processes” Apple patent just released on September 5th, 2019 goes a long way on how the Apple U1 Chip will be used with Apple Pay to begin and complete a sale. Apple Stores are already testing AppleLocate tags in their stores today. Although it takes some interesting radio frequency equipment to find them, I have been successful at two locations. The use case will allow for you to find a product like you world on a website with a whimsical Balloon, also used in the Find My app, to direct you to the precise location of the Apple product. With FaceID and Apple Pay you just look at your phone and confirm and leave. It is not hard to imagine many retail businesses adopting the system. It is also not hard to imagine AppleLocate used in industrial locations and medical locations. I will have much to say about this over the next few months as I have studied these use cases in depth for over a decade.Finally it is entirely likely a form of UWB will fully replace NFC for many payment transactions in the physical world. Although this will not take place any time in the next few years because the merchant payment systems are always on a 5 to 8 year upgrade cycle, however it will be likely to take place in the next decade.Open Sourced Apple UWB TechnologyI think it is likely some of the technology around the AppleLocate system and the Apple U1 Chip will be open sourced for adoption of other companies. Much like elements of AirPlay it would make a great deal of sense for Apple to get as many manufactures to adopt this system as possible. Apple is light years ahead of Google, Samsung and Amazon in intellectual property and public patents that would give them a big edge in home automation and help guide Siri to a more favorable position in the home.Speculative But Very Likely Use Cases Of The Apple U1 ChipI have written a few reports over the last few years on how UWB technology will be used in the future. I presented the rather certain use cases in this article, however there will be many others. Time and space constraints presented here I will mention a few:Bitcoin wallets and merchant payment systemsMedical biometricsVoice First HyperLocal HyperContextual systemsWhy Apple Did Not Announce The U1 Chip?So with all of these amazing attributes, why did Apple not announce the Apple U1 Chip? I assert it is a confluence of things:The iPhoneOS software needed is not yet releasedApple will release AppleLocate tags for holiday shopping 2019Apple had too many things to announce at this Apple Event and this would take too much timeApple is aware of the privacy implication many will cast and wants to spend more time to explainOther issues I can not present at this moment in timeThus it was not in the cards to hear anyone on the stage talk about the Apple U1 Chip but it certainly was presented behind Phil during his time on the stage and later indirectly communicated as part of a new version of “directional” AirDrop. Some people find it intriguing that AirDrop will use the Apple U1 Chip, however it will make much more sense how AirDrop will work in an AR/MR world that is on the map for Apple. In the meantime, being able to precisely locate via people and device via AirDrop will allow for new ways to send larger amounts of private and encrypted data.The findable abilities of the U1 chip will bridge across a multitude of Apple systems and software. It begins with AirDrop:, the largest file sharing social media network for the 14-23 age cohort. Quite hidden on campuses across the US AirDrop and Apple Messages serve as an ad-hoc HyperLocal HyperPrivate social network. Built into all new iPhones is this new permission based HyperLocal Social Network with permission based people finder systems built-in. Before we hear how bad this is, understand this is permission based and by invitation.The new AirDrop will allow for extremely high resolution to discover and send files and other new elements to friends and devices in a room. The precision is within millimeters. This will be achieved by simply pointing the phone in the direction of the person and a screen animation will allow in a held or worn (think Apple glasses) position to visualize the direction and avatar of the recipient.Specimen of the new AirDrop location system.We may begin to see the Apple U1 Chip take shape as early as September 30th, 2019 but more likely over the last quarter of 2019 on to the announcements of Apple Glasses.The Biggest Apple Announcement Today Was What Apple Actually Didn’t Announce—Yet.Apple began to give developers a small insight on the future of some aspects of the Appel U1 Chip during the World Wide Developer Conference 2019 {7]. The talk on “Introducing the Indoor Maps Program” will begin to more sense today. In the talk there was indirect reference to how Apple may use this technology.The accelerometer systems, GPS systems and IR proximity sensors of the first iPhone helped define the last generation of products. The Apple U1 Chip will be a material part of defining the next generation of Apple products.This all will move us to the ambient world of computing where the mechanical user interface will become less needed and situational images and video will present on the closest Apple U1 Chip enabled display. In some ways most of this is already here. I was urged to post a sort of recursive video below about this Quora answer as an example of the non-mechanical future.My drive this morning with Ms. #TheIntelligenceAmplifier.I was remiss and did not have her build my last 24 hours better but she is up to the task and found some solutions.I seriously don’t know what I would do with out her and— Brian Roemmele (@BrianRoemmele) September 12, 2019Specimen real-time video interaction with Agatha.Best, The Intelligence Amplifier from my garage lab.I feel rather strongly the Apple U1 Chip, over time will be seen as one of the most important aspect of the September 10th, 2019 Apple Event. We will see it as the start of the HyperLocal world of computing that ultimately will lead to less of a need for the cloud. The Apple U1 Chip is the start of this process of HyperLocal and HyperContextual computing where holographic crystal memory [8] and very fast local computer speed will render the cloud as we know it redundant and far less useful. With Petabytes of data on every device, all of your data and a useful base of the Internet will be local in a chip, on the device. This is far more than the speculated IoT edge computing and the Apple U1 Chip is one important part to bring this about. We will once again leave the Mainframe computer and become—cloudless.[0] Brian Roemmele's answer to Why is Apple’s iPhone Touch ID Important?[1] Ultra-wideband - Wikipedia[2] Brian Roemmele's answer to What are some interesting applications that are enabled by iBeacons technology featured in iOS 7?[3] DW1000 Radio IC - Decawave[4] Brian Roemmele's post in Accepting Payments[5] Hey, Apple and Google: iRobot wants to sell its Roomba vacuum indoor mapping data[6][7] Introducing the Indoor Maps Program - WWDC 2019 - Videos - Apple Developer[8] Formation of holographic polymer-dispersed liquid crystal memory by angle-multiplexing recording for optically reconfigurable gate arrays

Is technology making us less happy?

I'm going to assume that by saying "technology", you mean mobile phones, PCs, email, and social networking (therefore consumer technology). (Otherwise, even a butter knife is considered to be technology, and those make people quite happy ;) )Short argument: Technology use isolates us and discourage interaction. This interaction breeds discontent. Thankfully, we are slowly using technology to create tools that enable more face to face interaction (and happiness).Elaborated:Technology does isolate us and cut us off from human interaction. Face to face conversation, touch, laughing, eye contact, dancing are analog things that make most people happier. Nowadays, cafes, busses, sidewalks, and all sorts of public spaces are filled with people looking at screens instead of interacting. Some are presumably doing work so they can then be happy later with their friends/family, but many seem to be surfing and facebooking. Granted, these things don't make people explicitly unhappy, but I'd argue all this screen time worsens public life, which then makes people less happy.Kevin Kelly (Wired mag's co-founder) recently wrote a book about technology called What Technology Wants. He addressed current technology excesses at a talk I went to in Seattle. He argues that we need time to adjust our mores and norms to new technology. New pieces of technology, like the smartphone, sort of blindside society and people don't know how to behave with them (see cafes and busses in 2011). It takes a while before we learn to moderate and adapt our use of the new technology. Kelly gives the example of the TV and the Pulp Romance Novel as being pieces of technology that were first seen as being extremely disruptive to normal life, but that we eventually learned to moderate and live with. Kelly pointed out that much of our new disruptive technology (like the smartphone and the laptop) are less than a couple thousand days old; we simply haven't had time to adjust to them.For now, we're still on the extreme end of smart phone and laptop use. It's considered completely acceptable to for everyone to ignore everyone when they use their phone or computer in a public space. It's not even considered all that rude or unusual to walk down the sidewalk while looking at your smartphone (instead of the other pedestrians).But the shift towards moderated tech use is already happening. It seems to be becoming far less common to have someone text or take a call when you're talking to them in person. I give you the Phonekerchief, a bag (presumably stiched full of metal) that blocks a cell phone's signal so as to encourage undivided attention of someone you are with. This is an example of us trying to moderate cell phone's anti-social effects.There are quite a few web start-ups that are trying to use the internet in order to make opportunities for people to get off the internet and interact in is not a start up any longer, but they are one of the initiators of this type of business. Their tagline used to be "Use the internet to get off the internet".Nextdoor (service) is creating online community spaces for physical neighborhoods, with the hopes the internet can foster more conversations and better dynamics between neighbors. To me, this is exciting. More Potlucks, parties or other in-person events or simple interactions would probably make people a lot happier as they got a chance to bond with their neighbors.Grubwithus allows one to meet new people via eating a meal together. You prepay a meal for around 20 bucks and get a non-committal fairly natural way to interact with strangers.Couch Surfing's social capital benefits could be estimated in the millions according to some analysts.Conclusion: Consumer tech and social networking seems to have hurt public space and impromptu bantering/interaction with strangers, but there are a slew of new technology companies that trying to give us the tools to have more in-person interaction and thus, be happier.note: I welcome edits/suggestions to this post...

How can deep neural networks be applied to healthcare?

Throughout the course of comprehensive healthcare, many patients develop problems with their minds and bodies that can lead to severe discomfort, costly treatment, disabilities, and more. Predicting those escalations in advance offers healthcare providers the opportunity to apply preventative measure that might improve patient safety, and quality of care, while lowering medical costs. In simple terms, prediction using networks of big data used to evaluate specific people, and specific risk factors in certain illnesses could save lives, and avoid medical complications.Today, many prognostics methods turn to Artificial Neural Networks when attempting to find new insights into the future of patient healthcare. ANNs (Artificial Neural Networks) are just one of the many models being introduced into the field of healthcare by innovations like AI and big data. Their purpose is to transform huge amounts of raw data into useful decisions for treatment and care.What is a Neural Network?Understanding Neural Networks can be very difficult. After all, to many people, these examples of Artificial Intelligence in the medical industry are a futuristic concept.According to Wikipedia (the source of all truth) :“Neural Networks are a computational approach which is based on a large collection of neural units loosely modeling the way the brain solves problems with large clusters of biological neurons connected by axons. Each neural unit is connected with many others…These systems are self-learning and trained rather than explicitly programmed…”By – Own work, Derivative of File:Artificial neural network.svg (, CC BY-SA 3.0, LinkOne way to think of it is this: Imagine that a doctor wants to make a prediction regarding a patient’s health – for instance, whether she or he is at risk of suffering from a certain disease. How would a doctor be able to ascertain that information? In most cases, it would involve using blood tests, taking tests of the patient’s vitals, and more to identify features that have proven to be good predictors of patient health. However, what if doctors only know a handful of risk-factors for a specific disease – or worse, they don’t know the risk factors at all? It would be impossible to make predictions.ANNs help to provide the predictions in healthcare that doctors and surgeons simply couldn’t address alone. They work in moments wherein we can collect data, but we don’t understand which pieces of that data are vitally important yet. These abstractions can therefore capture complex relationships that might not be initially obvious – leading to better prediction for public health.What are the Possibilities for Neural Networks in Healthcare?Though they may seem like a futuristic concept, ANNs have been used in healthcare for several decades. In fact, the book “Neural Networks in Healthcare” covers the various uses of this system prior to 2006. Before 2006, the main successes of ANNs were found in areas like speech processing and image processing. Today, as new technologies emerge, capable of changing the way that we approach neural networks in the first place – it’s worth noting that there may be numerous new options for changing the industry. Today, the possibilities for Neural Networks in Healthcare include:Diagnostic systems – ANNs can be used to detect heart and cancer problems, as well as various other diseases informed by big data.Biochemical analysis – ANNs are used to analyze urine and blood samples, as well as tracking glucose levels in diabetics, determining ion levels in fluids, and detecting various pathological conditions.Image analysis – ANNs are frequently used to analyze medical images from various areas of healthcare, including tumor detection, x-ray classifications, and MRIs.Drug development – Finally, ANNs are used in the development of drugs for various conditions – working by using large amounts of data to come to conclusions about treatment options.Current Examples of Neural NetworksNeural networks can be seen in most places where AI has made steps within the healthcare industry. For instance, in the world of drug discovery, Data Collective and Khosla Ventures are currently backing the company “Atomwise“, which uses the power of machine learning and neural networks to help medical professionals discover safer and more effective medicines fast. The company recently published its first findings of Ebola treatment drugs last year, and the tools that Atomwise uses can tell the difference between toxic drug candidates and safer options.Similarly, options are being found that could insert neural networks into the realm of diagnostic. For instance, in 2014, Butterfly Networks, which are transforming the diagnostic realm with deep learning, devices, and the cloud, raised $100M for their cause. This organization currently works at the heart of the medicine and engineering sectors by bringing together world-class skills in everything from electrical engineering, to mechanical engineering, and medicine. At the same time, iCarbonX are developing artificial intelligence platforms to facilitate research relating to the treatment of various diseases and preventative care. The company believe that soon they will be able to help enable the future of truly personalized medicine.The Future of Healthcare…Perhaps the most significant problem with ANNs is that the learned features involved when it comes to assessing huge amounts of data can sometimes be difficult to interpret. This is potentially why ANNs are more commonly used during situations wherein we have a lot of data to ensure that the observed data doesn’t contain too many “flukes”. Think of it this way – if you toss a coin three times and receive “tails” every time, this doesn’t mean that a coin only has a “tails” side. It just means that you need further evaluation and more testing to get a proper reading of probability.ANNs are going to need some tweaking if they’re going to become the change that the healthcare industry needs. However, alongside new AI developments, it seems that neural networks could have a very important part to play in the future of healthcare.Healthcare organizations of all sizes, types, and specialties are becoming increasingly interested in how artificial intelligence can support better patient care while reducing costs and improving efficiencies.Over a relatively short period of time, the availability and sophistication of AI has exploded, leaving providers, payers, and other stakeholders with a dizzying array of tools, technologies, and strategies to choose from.Just learning the lingo has been a top challenge for many organizations.There are subtle but significant differences between key terms such as AI, machine learning, deep learning, and semantic computing.Understanding exactly how data is ingested, analyzed, and returned to the end user can have a big impact on expectations for accuracy and reliability, not to mention influencing any investments necessary to whip an organization’s data assets into shape.In order to efficiently and effectively choose between vendor products or hire the right data science staff to develop algorithms in-house, healthcare organizations should feel confident that they have a firm grasp on the different flavors of artificial intelligence and how they can apply to specific use cases.Deep learning is a good place to start. This branch of artificial intelligence has very quickly become transformative for healthcare, offering the ability to analyze data with a speed and precision never seen before.But what exactly is deep learning, how does it differ from other machine learning strategies, and how can healthcare organizations leverage deep learning techniques to solve some of the most pressing problems in patient care?DEEP LEARNING IN A NUTSHELLDeep learning, also known as hierarchical learning or deep structured learning, is a type of machine learning that uses a layered algorithmic architecture to analyze data.In deep learning models, data is filtered through a cascade of multiple layers, with each successive layer using the output from the previous one to inform its results. Deep learning models can become more and more accurate as they process more data, essentially learning from previous results to refine their ability to make correlations and connections.Deep learning is loosely based on the way biological neurons connect with one another to process information in the brains of animals. Similar to the way electrical signals travel across the cells of living creates, each subsequent layer of nodes is activated when it receives stimuli from its neighboring neurons.In artificial neural networks (ANNs), the basis for deep learning models, each layer may be assigned a specific portion of a transformation task, and data might traverse the layers multiple times to refine and optimize the ultimate output.These “hidden” layers serve to perform the mathematical translation tasks that turn raw input into meaningful output.An illustration of a deep learning neural networkSource: University of Cincinnati“Deep learning methods are representation-learning methods with multiple levels of representation, obtained by composing simple but non-linear modules that each transform the representation at one level (starting with the raw input) into a representation at a higher, slightly more abstract level,” explains a 2015 article published in Nature, authored by engineers from Facebook, Google, the University of Toronto, and Université de Montréal.“With the composition of enough such transformations, very complex functions can be learned. Higher layers of representation amplify aspects of the input that are important for discrimination and suppress irrelevant variations.”This multi-layered strategy allows deep learning models to complete classification tasks such as identifying subtle abnormalities in medical images, clustering patients with similar characteristics into risk-based cohorts, or highlight relationships between symptoms and outcomes within vast quantities of unstructured data.Unlike other types of machine learning, deep learning has the added benefit of being able to decisions with significantly less involvement from human trainers.While basic machine learning requires a programmer to identify whether a conclusion is correct or not, deep learning can gauge the accuracy of its answers on its own due to the nature of its multi-layered structure.“With the composition of enough such transformations, very complex functions can be learned.”Deep learning also requires less preprocessing of data. The network itself takes care of many of the filtering and normalization tasks that must be completed by human programmers when using other machine learning techniques.“Conventional machine-learning techniques are limited in their ability to process natural data in their raw form,” said the article from Nature.“For decades, constructing a pattern-recognition or machine-learning system required careful engineering and considerable domain expertise to design a feature extractor that transformed the raw data (such as the pixel values of an image) into a suitable internal representation or feature vector from which the learning subsystem, often a classifier, could detect or classify patterns in the input.”Deep learning networks, however, “automatically discover the representations needed for detection or classification,” reducing the need for supervision and speeding up the process of extracting actionable insights from datasets that have not been as extensively curated.Naturally, the mathematics involved in developing deep learning models are extraordinarily intricate, and there are many different variations of networks that leverage different sub-strategies within the field.The science of deep learning is evolving very quickly to power some of the most advanced computing capabilities in the world, spanning every industry and adding significant value to user experiences and competitive decision-making.WHAT ARE THE USE CASES FOR DEEP LEARNING IN HEALTHCARE?Many of the industry’s deep learning headlines are currently related to small-scale pilots or research projects in their pre-commercialized phases.However, deep learning is steadily finding its way into innovative tools that have high-value applications in the real-world clinical environment.Some of the most promising use cases include innovative patient-facing applications as well as a few surprisingly established strategies for improving the health IT user experience.Imaging analytics and diagnosticsOne type of deep learning, known as convolutional neural networks (CNNs), is particularly well-suited to analyzing images, such as MRI results or x-rays.CNNs are designed with the assumption that they will be processing images, according tocomputer science experts at Stanford University, allowing the networks to operate more efficiently and handle larger images.As a result, some CNNs are approaching – or even surpassing – the accuracy of human diagnosticians when identifying important features in diagnostic imaging studies.In June of 2018, a study in the Annals of Oncology showed that a convolutional neural network trained to analyze dermatology images identified melanoma with ten percent more specificity than human clinicians.Even when human clinicians were equipped with background information on patients, such as age, sex, and the body site of the suspect feature, the CNN outperformed the dermatologists by nearly 7 percent.“Our data clearly show that a CNN algorithm may be a suitable tool to aid physicians in melanoma detection irrespective of their individual level of experience and training,” said the team of researchers from a number of German academic institutions.In addition to being highly accurate, deep learning tools are fast.Researchers at the Mount Sinai Icahn School of Medicine have developed a deep neural network capable of diagnosing crucial neurological conditions, such as stroke and brain hemorrhage, 150 times faster than human radiologists.“Our data clearly show that a CNN algorithm may be a suitable tool to aid physicians in melanoma detection irrespective of their individual level of experience and training.”The tool took just 1.2 seconds to process the image, analyze its contents, and alert providers of a problematic clinical finding.“The expression ‘time is brain’ signifies that rapid response is critical in the treatment of acute neurological illnesses, so any tools that decrease time to diagnosis may lead to improved patient outcomes,” said Joshua Bederson, MD, Professor and System Chair for the Department of Neurosurgery at Mount Sinai Health System and Clinical Director of the Neurosurgery Simulation Core.Deep learning is so adept at image work that some AI scientists are using neural networks to create medical images, not just read them.A team from NVIDIA, the Mayo Clinic, and the MGH & BWH Center for Clinical Data Science has developed a method of using generative adversarial networks (GANs), another type of deep learning, which can create stunningly realistic medical images from scratch.The images use patterns learned from real scans to create synthetic versions of CT or MRI images. The data can be randomly generated and endlessly diverse, allowing researchers to access large volumes of necessary data without any concerns around patient privacy or consent.These simulated images are so accurate that they can help train future deep learning models to diagnose clinical findings.“Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models,” said the team. “We propose a method to generate synthetic abnormal MRI images with brain tumors by training a generative adversarial network.”“This offers an automatable, low-cost source of diverse data that can be used to supplement the training set. For example, we can alter a tumor’s size, change its location, or place a tumor in an otherwise healthy brain, to systematically have the image and the corresponding annotation.”Such a strategy could significantly reduce of AI’s biggest sticking points: a lack of reliable, sharable, high-volume datasets to use for training and validating machine learning models.Natural language processingDeep learning and neural networks already underpin many of the natural language processing tools that have become popular in the healthcare industry for dictating documentation and translating speech-to-text.Because neural networks are designed for classification, they can identify individual linguistic or grammatical elements by “grouping” similar words together and mapping them in relation to one another.This helps the network understand complex semantic meaning. But the task is complicated by the nuances of common speech and communication. For example, words that always appear next to each other in an idiomatic phrase, may end up meaning something very different than if those same words appeared in another context (think “kick the bucket” or “barking up the wrong tree”).While acceptably accurate speech-to-text has become a relatively common competency for dictation tools, generating reliable and actionable insights from free-text medical data is significantly more challenging.Unlike images, which consist of defined rows and columns of pixels, the free text clinical notes in electronic health records (EHRs) are notoriously messy, incomplete, inconsistent, full of cryptic abbreviations, and loaded with jargon.Currently, most deep learning tools still struggle with the task of identifying important clinical elements, establishing meaningful relationships between them, and translating those relationships into some sort of actionable information for an end user.A recent literature review from JAMIA found that while deep learning surpasses other machine learning methods for processing unstructured text, several significant challenges, including the quality of EHR data, are holding these tools back from true success.“Researchers have confirmed that finding patterns among multimodal data can increase the accuracy of diagnosis, prediction, and overall performance of the learning system. However, multimodal learning is challenging due to the heterogeneity of the data,” the authors observed.Accessing enough high-quality data to train models accurately is also problematic, the article continued. Data that is biased or skewed towards particular age groups, ethnicities, or other characteristics could create models that are not equipped to accurately assess a broad variety of real-life patients.Still, deep learning represents the most promising pathway forward into trustworthy free-text analytics, and a handful of pioneering developers are finding ways to break through the existing barriers.A team from Google, UC San Francisco, Stanford Medicine, and the University of Chicago Medicine, for example, developed a deep learning and natural language processing algorithm that analyzed more than 46 billion data points from more than 216,000 EHRs across two hospitals.The tool was able to improve on the accuracy of traditional approaches for identifying unexpected hospital readmissions, predicting length of stay, and forecasting inpatient mortality.“This predictive performance was achieved without hand-selection of variables deemed important by an expert, similar to other applications of deep learning to EHR data,” the researchers said.“Instead, our model had access to tens of thousands of predictors for each patient, including free-text notes, and identified which data were important for a particular prediction.”While the project is only a proof-of-concept study, Google researchers said, the findings could have dramatic implications for hospitals and health systems looking to reduce negative outcomes and become more proactive about delivering critical care.Drug discovery and precision medicinePrecision medicine and drug discovery are also on the agenda for deep learning developers. Both tasks require processing truly enormous volumes of genomic, clinical, and population-level data with the goal of identifying hitherto unknown associations between genes, pharmaceuticals, and physical environments.Deep learning is an ideal strategy for researchers and pharmaceutical stakeholders looking to highlight new patterns in these relatively unexplored data sets – especially because many precision medicine researchers don’t yet know exactly what they should be looking for.“Our model had access to tens of thousands of predictors for each patient, including free-text notes, and identified which data were important for a particular prediction.”The world of genetic medicine is so new that unexpected discoveries are commonplace, creating an exciting proving ground for innovative approaches to targeted care.The National Cancer Institute and the Department of Energy are embracing this spirit of exploration through a number of joint projects focused on leveraging machine learning for cancer discoveries.The combination of predictive analytics and molecular modeling will hopefully uncover new insights into how and why certain cancers form in certain patients.Deep learning technologies will accelerate the process of analyzing data, the two agencies said, shrinking the processing time for key components from weeks or months to just a few hours.The private sector is similarly committed to illustrating how powerful deep learning can be for precision medicine.A partnership by GE Healthcare and Roche Diagnostics, announced in January of 2018, will focus on using deep learning and other machine learning strategies to synthesize disparate data sets critical to developing precision medicine insights.The two companies will work to combine in-vivo and in-vitro data, EHR data, clinical guidelines, and real-time monitoring data to support clinical decision-making and the creation of more effective, less invasive therapeutic pathways.“By leveraging this combined data set using machine learning and deep learning, it may be possible in the future to reduce the number of unnecessary biopsies that are performed due to suspicious findings in the mammograms and possibly also reduce mastectomies that are performed to combat ductal carcinoma in situ, a condition that may evolve into invasive breast cancer in some cases,” said Nadeem Ishaque, Chief Innovation Officer, GE Healthcare Imaging.A separate study, conducted by researchers from the University of Massachusetts and published in JMIR Medical Informatics, found that deep learning could also identify adverse drug events (ADEs) with much greater accuracy than traditional models.“By leveraging this combined data set using machine learning and deep learning, it may be possible in the future to reduce the number of unnecessary biopsies.”The tool combines deep learning with natural language processing to comb through unstructured EHR data, highlighting worrisome associations between the type, frequency, and dosage of medications. The results could be used for monitoring the safety of novel therapies or understanding how new pharmaceuticals are being prescribed in the real-world clinical environment.Clinical decision support and predictive analyticsIn a similar vein, the industry has high hopes for the role of deep learning in clinical decision support and predictive analytics for a wide variety of conditions.Deep learning may soon be a handy diagnostic companion in the inpatient setting, where it can alert providers to changes in high-risk conditions such as sepsis and respiratory failure.Researchers from the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) have created a project called ICU Intervene, which leverages deep learning to alert clinicians to patient downturns in the critical care unit.“Much of the previous work in clinical decision-making has focused on outcomes such as mortality (likelihood of death), while this work predicts actionable treatments,” said PhD student and lead author Harini Suresh. “In addition, the system is able to use a single model to predict many outcomes.”The tool offers human clinicians a detailed rationale for its recommendations, helping to foster trust and allowing providers to have confidence in their own decision-making when potentially overruling the algorithm.Google is also on the leading edge of clinical decision support, this time for eye diseases. The company’s UK-based subsidiary, DeepMind, is working to develop a commercialized deep learning CDS tool that can identify more than 50 different eye diseases – and provide treatment recommendations for each one.In a supporting study published in Nature, DeepMind and Moorfields Eye Hospital found that the tool is just as accurate as a human clinician, and has the potential to significantly expand access to care by reducing the time it takes for an exam and diagnosis.“Currently, eye care professionals use optical coherence tomography (OCT) scans to help diagnose eye conditions. These 3D images provide a detailed map of the back of the eye, but they are hard to read and need expert analysis to interpret,” explained DeepMind.“The time it takes to analyze these scans, combined with the sheer number of scans that healthcare professionals have to go through (over 1,000 a day at Moorfields alone), can lead to lengthy delays between scan and treatment – even when someone needs urgent care. If they develop a sudden problem, such as a bleed at the back of the eye, these delays could even cost patients their sight.”With deep learning, the triage process is nearly instantaneous, the company asserted, and patients do not have to sacrifice quality of care.“This is a hugely exciting milestone, and another indication of what is possible when clinicians and technologists work together,” DeepMind said.WHAT IS THE FUTURE OF DEEP LEARNING IN HEALTHCARE?As intriguing as these pilots and projects can be, they represent only the very beginning of deep learning’s role in healthcare analytics.Excitement and interest about deep learning are everywhere, capturing the imaginations of regulators and rule makers, private companies, care providers, and even patients.The Office of the National Coordinator (ONC) is one organization with particularly high hopes for deep learning, and it is already applauding some developers for achieving remarkable results.In a recent report on the state of AI in the healthcare setting, the agency noted that some deep learning algorithms have already produced “transformational” outcomes.“There have been significant demonstrations of the potential utility of artificial Intelligence approaches based on deep learning for use in medical diagnostics,” the report said.“Where good training sets represent the highest levels of medical expertise, applications of deep learning algorithms in clinical settings provide the potential of consistently delivering high quality results.”The report highlighted early successes in diabetic retinal screenings and the classification of skin cancer as two areas where deep learning may already be changing the status quo.On the clinical side, imaging analytics is likely to be the focal point for the near future, due to the fact that deep learning already has a head start on many high-value applications.“Applications of deep learning algorithms in clinical settings provide the potential of consistently delivering high quality results.”But purely clinical applications are only one small part of how deep learning is preparing to change the way the healthcare system functions.The strategy is integral to many consumer-facing technologies, such as chatbots, mHealth apps, and virtual personalities like Alexa, Siri, and Google Assistant.These tools have the potential to radically alter the way patients interact with the healthcare system, offering home-based chronic disease management programming, 24/7 access to basic triage, and new ways to complete administrative tasks.By 2019, up to 40 percent of businesses are planning to integrate one or more of these popular consumer technologies into their internal or external workflows.Customer support and communication are two early implementations. But with market-movers like Amazon rumored to start rolling out more consumer-facing health options to patients, it may only be a matter of time before chatting with Alexa becomes as common as shooting the breeze with a medical assistant.Voice recognition and other analytics based on deep learning also have the near-term potential to provide some relief to physicians and nurses struggling with their EHRs.Google appears particularly interested in capturing medical conversations in the clinic and using deep learning to reduce administrative burdens on providers.One recent research paper illustrated the potential to use deep learning and NLP to understand casual conversation in a noisy environment, giving rise to the possibility of using an ambient, intelligent scribe to shoulder the onus of documentation.“We wondered: could the voice recognition technologies already available in Google Assistant, Google Home, and Google Translate be used to document patient-doctor conversations and help doctors and scribes summarize notes more quickly?” a Google team posited.“While most of the current automatic speech recognition (ASR) solutions in medical domain focus on transcribing doctor dictations (i.e., single speaker speech consisting of predictable medical terminology), our research shows that it is possible to build an ASR model which can handle multiple speaker conversations covering everything from weather to complex medical diagnosis,” the blog post says.Google will work with physicians and data scientists at Stanford to refine the technology and understand how it can be best applied to the clinical setting.“We hope these technologies will not only help return joy to practice by facilitating doctors and scribes with their everyday workload, but also help the patients get more dedicated and thorough medical attention, ideally, leading to better care,” the team said.EHR vendors are also taking a hard look at how machine learning can streamline the user experience by eliminating wasteful interactions and presenting relevant data more intuitively within the workflow.“Taking out the trash” by using artificial intelligence to learn a user’s habits, anticipate their needs, and display the right data at the right time is a top priority for nearly all of the major health IT vendors – vendors who are finding themselves in the hot seat as unhappy customers plead for better solutions for their daily tasks.Both patients and providers are demanding much more consumer-centered tools and interactions from the healthcare industry, and artificial intelligence may now be mature enough to start delivering.“We finally have enough affordable computing power to get the answers we’re looking for,” said James Golden, PhD, Senior Managing Director for PwC’s Healthcare Advisory Group, to Healthcare IT Analytics News on Healthcare BI, Population Health and Data Management in February of 2018.“When I did my PhD in the 90s on back propagation neural networks, we were working with an input layer, an output layer, and two middle layers,” he recalled.“That’s not extremely complex. But it ran for four days on an Apple Lisa before producing results. I can do the same computation today in a picosecond on an iPhone. That is an enormous, staggering leap in our capabilities.”The intersection of more advanced methods, improved processing power, and growing interest in innovative methods of predicting, preventing, and cheapening healthcare will likely bode well for deep learning.With an extremely high number of promising use cases, strong investment from major players in the industry, and a growing amount of data to support cutting-edge analytics, deep learning will no doubt play a central role in the quest to deliver the highest possible quality care to consumers for decades to come.References :What Is Deep Learning and How Will It Change Healthcare?

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