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Google believes that mobile and digital-first experiences will be the future of health, and it has stats to back it up—namely the millions of questions asked in search queries, and the billions of views on health-related videos across its video streaming platform, YouTube.
The tech giant has nonetheless had a bumpy journey in its pursuit to turn information into useful tools and services. Google Health, the official unit that the company formed in 2023 to tackle this issue, dissolved in 2023. Still, the mission lived on in bits across YouTube, Fitbit, Health AI, Cloud, and other teams.
Google is not the first tech company to dream big when it comes to solving difficult problems in healthcare. IBM, for example, is interested in using quantum computing to get at topics like optimizing drugs targeted to specific proteins, improving predictive models for cardiovascular risk after surgery, and cross-searching genome sequences and large drug-target databases to find compounds that could help with conditions like Alzheimer’s.
[Related: Google Glass is finally shattered]
In Google’s third annual health event on Tuesday, called “The Check Up,” company executives provided updates about a range of health projects that they have been working on internally, and with partners. From a more accurate AI clinician, to added vitals features on Fitbit and Android, here are some of the key announcements.
A demo of how Google’s AI can be used to guide pregnancy ultrasound. Charlotte Hu
For Google, previous research at the intersection of AI and medicine have covered areas such as breast cancer detection, skin condition diagnoses, and the genomic determinants of health. Now, it’s expanding its AI models to include more applications, such as cancer treatment planning, finding colon cancer from images of tissues, and identification of health conditions on ultrasound.
[Related: Google is launching major updates to how it serves health info]
Even more ambitiously, instead of using AI for a specific healthcare task, researchers at Google have also been experimenting with using a generative AI model, called Med-PaLM, to answer commonly asked medical questions. Med-PaLM is based on a large language model Google developed in-house called PaLM. In a preprint paper published earlier this year, the model scored 67.6 percent on a benchmark test containing questions from the US Medical License Exam.
At the event, Alan Karthikesalingam, a senior research scientist at Google, announced that with the second iteration of the model, Med-PaLM 2, the team has bumped its accuracy on medical licensing questions to 85.4 percent. Compared to the accuracy of human physicians, sometimes Med-PaLM is not as comprehensive, according to clinician reviews, but is generally accurate, he said. “We’re still learning.”
An example of Med-PaLM’s evaluation. Charlotte Hu
In a language model realm, although it’s not the buzzy new Bard, a conversational AI called Duplex is being employed to verify whether providers accept federal insurance like Medicaid, boosting a key search feature Google first unveiled in December 2023.
[Related: This AI is no doctor, but its medical diagnoses are pretty spot on]
On the consumer hardware side, Google devices like Fitbit, Pixel, and Nest will now be able to provide users with an extended set of metrics regarding their heart rate, breathing, skin temperature, sleep, stress, and more. For Fitbit, the sensors are more evident. But the cameras on Pixel phones, as well as the motion and sound detectors on Nest devices, can also give personal insights on well-being. Coming to Fitbit’s sleep profile feature is a new metric called stability, which tells users when they’re waking up in the night by analyzing their movement and heart rate. Google also plans to make a lot more of its health metrics, like respiration, which uses a camera and non-AI algorithms to detect movement and track pixels, and heart rate, which relies on an algorithm that measures and changes in skin color, available to users with compatible devices without a subscription.
Users can take their pulse by placing their fingertip over the back cameras of their Pixel phones. Charlotte Hu
This kind of personalization around health will hopefully allow users to get feedback on long-term patterns and events that may deviate from their normal baseline. Google is testing new features too, like an opt-in function for identifying who coughed, in addition to counting and recording coughs (both of which are already live) on Pixel. Although it’s still in the research phase, engineers at the company say that this feature can register the tone and timber of the cough as vocal fingerprints for different individuals.
Watch the full keynote below:
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This year’s Halloween was made scarier by the rising COVID-19 infection rates across the country, and heading into the holiday season those numbers are only expected to rise. As similar surges in Europe are forcing many nations back into lockdown, the US is gearing up for an incredibly important presidential election.Household spread of COVID-19 is common and quick, study finds
A new study published in the CDC’s Mortality and Morbidity Weekly Report last week found that transmission of the novel coronavirus between household members occurs more often and more quickly than previously thought.
The study trained 101 participants, as well as 191 people who lived in the same households as those participants, to carry out nasal swab or saliva tests on themselves daily. These people also filled out a daily log of any symptoms. The results showed that 53 percent of those living with infected people became infected, 75 percent of whom became infected in fewer than five days (though not everyone who participated in the study self-isolated in their home). Previous research estimated that infection among household members only occurred 20-40 percent of the time.
These findings provide further evidence that if you suspect that you have been exposed to COVID-19, you should self-isolate within your home even before receiving test results in order to protect the people you live with—and if exposure occurs, your family or roommates should isolate as well to prevent further spread.Mortality rates from the coronavirus have dropped since March
In March and April, when the novel coronavirus was tearing through metropolitan areas of the US and hospitals were largely unprepared for the surge of infections, death rates for people in intensive care were as high as 25 percent. That number quickly fell to around seven percent as hospitals adapted to the pandemic.
Data is specifically from hospitalized patients, so this death rate doesn’t apply to the general population. Infographic by Sara Chodosh
Another study, published in August in the Journal of the American Medical Association, also found a decrease in death rate, from 12.1 to 5.1 percent among patients in Houston area hospitals.
However, that doesn’t mean that the virus isn’t dangerous. Researchers say that even after the decreases we’ve seen in the death rate, the novel coronavirus is still ten times more deadly than the flu and often comes with long-term complications that, since the virus is so new, remain largely unknown.The US passed 100,000 infections in a single day
On Friday, the US saw 100,000 COVID-19 infections in a single day, breaking its previous record for single-day infections. In the past week and a half, the US has set a new record for single-day infections five times.
That number brought the cumulative infections since the beginning of the pandemic to nine million, representing three percent of the US population. On Thursday, more than 1,000 people died of the virus. That was the third time in October that number was reached in a single day.
Last month, the infection rate was 57 percent higher than in September, and that number just keeps climbing. Experts continue to recommend that people wear masks, wash their hands frequently, and self-isolate as soon as possible after exposure to an infected person in order to slow the spread of the virus. Only we can stop this virus—but it takes everyone working together to be effective.
Putting investment into startups is a famously risky undertaking. Venture sponsored startups bomb 75% of the time, which frequently gives speculators motivation to delay when thinking about a venture into another business. Frequently, business entrepreneurs have little to go without their senses and constrained research. Luckily, artificial intelligence (AI) can make the investing procedure more sensible and successful for early-stage financial investors and give business visionaries insights when building another venture. Today, even small corporates produce a constant stream of data, from regular price variances in the stock to corporate declarations and that’s just the beginning. At the point when the data streams in, it could be extremely hard to select what is significant. How would you approach staying invested as a long-term investor? With time, a few financial specialists figure out how to work out vital data. At that point, they build up their very own pool of reliable sources which match their investment portfolio. Artificial intelligence can enable investors to decide how early-stage startups will perform and can in all respects rapidly make a summation of a startup’s likelihood for progress by evaluating its revenue growth, market size, industry experience, among different variables. It can analyze information to figure out what statistics will eventually bring about progress. This implies it can foresee investment-worthy startups before they even start raising money. Numerous financial investors are as of now utilizing AI to help settle on significant investment decisions. Through a mix of algorithms, data mining, and language processing, AI can build up relationships and patterns to make proposals based on investor’s inclinations. Since AI is continually accepting new data, it develops as it evaluates new information and becomes progressively precise and far-reaching. Motherbrain, an ML framework that EQT Ventures developed to recognize upcoming startups, applies its algorithm to historical data so as to distinguish promising investment applicants. The framework utilizes information, for example, financial data, web ranking, application positioning and social media activity to screen and analyze a lot of companies, something that would be difficult to do physically. Something that is intriguing is that if Motherbrain’s innovation had been accessible previously, the framework would have recognized Airbnb, Snapchat, and Stripe as worthwhile investment opportunities when the companies had just only got seed and angel funding. Knowledge and resources previously accessible just to significant firms would now be able to be accessed by small scale investors including angel investors. One of the real deterrents for venture capitalists and angel investors is finding fascinating investment targets before any other person. It is regularly an overwhelming and travel-escalated challenge. However, ML and predictive analytics are beginning to change that procedure. For other people, there are products, for example, algrow, an intelligent algorithmic investment that depends on AI which is totally free of human prejudice. It is a perfect investment product which consequently changes to an equity fund when the market is low for promising returns and to a debt fund when the market is high, in this way securing your assets. Indeed, even financial specialists who are consistently up-to-date with regards to market developments could get in a tough situation if there was an occurrence of inaccurate data or when vulnerability hits the market. These errors could be as noxious bits of gossip, financial frauds or even innocent slip-ups with respect to partnerships. Since the financial markets are acquainted with the persistent stream of data, vulnerability or an interference in the flow could prove to be more terrible than the awful news. So, what is confining the adoption of AI inside traditional firms following the achievement of hedge funds? Most prominent issues come down to a huge financial and human capital investment. Presumably, the most widely recognized deterrent is the absence of an accessible talent pool. As another field, there is a constrained ability pool with expertise and experience in the field. Same goes for the data scientists and AI specialists who are commonly expected to make an interpretation of the insights into significant business actions and estimates. Paysa reports that there are more than 10,000 open AI positions in the United States only, and IBM further conjectures that the number of related employment postings in the US will increase by 364,000 to 2.7 million.
Digital Employees are more competent than Chatbots
Financial institutions such as banks are at the forefront of technological innovations, looking for ways to execute faster and serve their customers better. It may be tempting to embrace whatever technology comes on the way in striving for the latest and greatest solutions. It has led to the proliferation of chatbots that claim to reinforce call centres withAdapt to Customers’ Needs Help Customers beyond Restrictions
Learning is another key factor that distinguishes digital employees from traditional chatbots. Chatbots, as a rigid system, don’t improve with time, nor can they assist in the form of a whisper executive that helps human employees answer customer queries and resolve problems faster and efficiently. As focused before the capability to handle unexpected is the key to driving NPS and first call resolution. The key differentiators for digital employees lay here. They can even go beyond the automation of simple tasks like troubleshooting and password resets. The capacity of helping customers unearth account details, process mortgage applications and introduce new products, digital employees are quickly turning into a personal concierge for every consumer. They can perform more than an FAQ alternative, applyingProvide Solution without Stumbling
One should not be confused between chatbots, true artificial intelligence and conversational artificial intelligence. These are not the same and do not deliver the same results. Wherein chatbots cannot handle the unexpected; digital employees can evolve with customer requirements.
Financial institutions such as banks are at the forefront of technological innovations, looking for ways to execute faster and serve their customers better. It may be tempting to embrace whatever technology comes on the way in striving for the latest and greatest solutions. It has led to the proliferation of chatbots that claim to reinforce call centres with automation . In reality, these bots behave like dated robots and are stubborn in how they communicate with customers, building an IVR 2.0 format that frustrates callers and does not allow banks from serving customers. Due to the pandemic and the possibility of subsequent lockdowns, this seems challenging. Most of the banks were inundated by an influx of calls at the height of the quarantine. Now they are facing a potential resurgence as many states and cities reconsider their decision to reopen. Even when call volumes are high, banks need to be able to serve the same quality of service over the phone as they would physically. However, banks cannot be expected to hire an overabundance of call centre executives for the smooth functioning of call volumes and satisfy customers with chatbots . They should instead monitor the power of digital employees, which can help them to meet customers’ expectations in ways that are impossible with any other technology. Conversational artificial intelligence backs digital employees, which differ from chatbots in numerous ways, starting with their ability to handle customer going off-script. Chatbots are unable to determine what customers want if they change their mind mid-sentence and come up with multiple issues or queries simultaneously. Being confused chatbots will either provide wrong answers or fail to give any answer at all. This could make consumers extremely frustrated, and their issues will only be worsened by the chatbot’s inability to resolve them. Chatbots are built with a strict, formulaic interaction in mind. They are not able to answer questions beyond their capacity in which they were programmed, nor can they learn to solve new issues over time. It inevitably drives roadblocks that reduce NPS scores and limit the number of contacts which can be sorted out on the first contact. Digital employees can adapt to customers’ needs, unlike a chatbot. Moreover, digital employees understand what the customer exactly referred to. For instance, if a customer says, “On Thursday, transfer $200 to Jack,” the digital employee will understand what it means. If the consumer then adds, “Transfer it on Friday instead via Ricky,” conversational artificial intelligence (AI) will understand what the customer meant, react accordingly and transfer the money via the requested service without further clarification. Digital employees can prioritise the most important aspects of a request as well. For example, if a customer says, “I cashed my loyalty points for a gift card and would prefer to know when it will ship. Due to a fraudulent charge on my account, I need to cancel that card.” Chatbots would not know how to respond; at best, a user might find out when the gift card ships. Digital employees can cut through the clutter and take immediate action on that fraudulent charge while identifying the dual intent to know when the gift card ships.Learning is another key factor that distinguishes digital employees from traditional chatbots. Chatbots, as a rigid system, don’t improve with time, nor can they assist in the form of a whisper executive that helps human employees answer customer queries and resolve problems faster and efficiently. As focused before the capability to handle unexpected is the key to driving NPS and first call resolution. The key differentiators for digital employees lay here. They can even go beyond the automation of simple tasks like troubleshooting and password resets. The capacity of helping customers unearth account details, process mortgage applications and introduce new products, digital employees are quickly turning into a personal concierge for every consumer. They can perform more than an FAQ alternative, applying Natural Language Processing to understand better what the human is trying to imply. Unlike a chatbot, digital employees will experience standstill if a customer asks more than one question at a chúng tôi should not be confused between chatbots, true artificial intelligence and conversational artificial intelligence. These are not the same and do not deliver the same results. Wherein chatbots cannot handle the unexpected; digital employees can evolve with customer requirements. Backed by conversational AI, digital employees can decipher complex sequence, recognise the intent and then provide solutions without stumbling or arriving at dead ends. If a problem comes up that it cannot resolve on its own, the digital employee is smart enough to hand it over to a human customer representative who can take the reins from there.
Google Play Protect’s AI works but needs to get better fast
This is going to be yet another Apple versus Google and iOS versus Android thing. Long story short, Apple manually screens every app submitted to its App Store and, as Valve just learned, not everyone makes the cut. It’s a painstaking process that results in a comparatively smaller number of apps (compared to Android) but has the explicitly assurance of safety, if not quality.
In contrast, Google is always more interested in numbers. It wants more but, at the same time, knows it won’t scale well to manual processes. Which is why it has entrusted Android’s security, or Google Play Store’s security rather, to machine learning. And it’s quite proud of what is has accomplished so far.
Google boasts that Play Protect’s systems scan over 50 billion apps daily. These scans are mostly done on the Play Store but also happen from time to time on Google-certified phones. The idea is simple: it uses automation, algorithms, and machine learning to root out Potentially Harmful Apps or PHAs. Because of that system, Google Play apps are 9 times less likely to be a PHA, or so says Google.
Of course, it all depends on how well that machine learning system is able to detect PHAs. It requires Google to feed it thousands and thousands of examples of both potentially harmful behavior as well as safe ones. And what better source for that data than Google Play Store itself and user’s (promised to be anonymized) data. These neural networks look for telltale signs of bad behavior, like interacting with other apps, downloading files in the background, or bypassing Android’s security features.
To its credit, Google Play Protect isn’t just a simple and single-minded bouncer. In addition to identifying PHAs, Google also groups malicious behavior into families. This system allows it to identify apps that have remained outside their radar but exhibit similar traits as known PHAs.
THat machine learning system has, so far, accurately detected 60.3% of the PHAs and malware Google Play Protect identified last year. It’s not hard to understand why Google is proud of that. That 60% was made by a machine with little to no direct human intervention. But 60% isn’t exactly an encouraging number and Google Play Store’s history might not inspire much confidence yet.
To be fair, this new AI-powered system was only added two years ago, long after Google Play Store was already notorious for having apps that slip through the cracks. Has it improved since then? It’s not that easy to say. News coverage has definitely been fewer, but that may be because people are tired of hearing about it over and over again. There are definitely some high-profile mishaps, but that could be attributed to the remaining 39.7% that didn’t get detected.
And then there’s the case that not all Android devices might even have Google Play Protect at all. As part of the Google Play bundle, it only benefits certified devices. There is quite a number of Android smartphones in the market that don’t and there might even be more to come if certain political forces have their way. It is definitely a cunning strategy to “encourage” OEMs to get Google-certified, but not all can afford to pay the price.
That’s not to downplay Google’s achievements but it definitely needs to pick up the pace. The world won’t wait for is machine learning systems to wise up, especially when privacy and security are being put under a microscope again. Given recent events, there is even more pressure on Google now to prove that its AI-driven system doesn’t just work but is also better than the competition. And, considering the number of apps in Google Play Store and the more than 2 billion Android devices out there, 60.3% just doesn’t cut it.
Cisco Systems has made a couple of announcements about mobility of late.
The first one, issued at the end of April (read it here), snuck past us initially, but we caught up with it. It describes a partnership with Nokia that “extends the rich Cisco Unified IP phone capabilities to Nokia Eseries dual-mode smartphones over Cisco Unified Wireless Networks, to offer users a seamless mobile experience in the enterprise environment and public cellular networks.”
Translation: With the help of some software from Cisco, Nokia’s dual-mode handsets will be able to place and receive phone calls over Cisco wireless LANs—when they’re in range—and save money on cellular minutes. In enterprise telephony parlance, this bit of technology will ‘port the desktop phone number’ to the Nokia device over Wi-Fi.
It’s nice to see Cisco taking the first steps toward mobilizing its formidable IP telephony capabilities. And in characterizing this dual-mode capability as “mobile unified communications” (a stretch in our view), the announcement constitutes at least a tacit endorsement of the idea that mobile phone users in the field should have access to the same communications resources they enjoy at their office desk.
But if the company wants to be a serious contender in anything that could legitimately be called mobile unified communications, Cisco is looking at a serious game of catch-up, as a generation of smaller, more nimble competitors has already got a formidable head start.
The first to take on the challenge of the dual-network telephony were the so-called fixed/mobile convergence (F/MC) vendors—startups such as Kineto Wireless and the other members of its Unlicensed Mobile Access coalition (UMA) or BridgePort Networks and other members of the Mobile Ignite trade group.
These companies figured out how to identify the “best available network” for a call and how to engineer automated, on-the-fly handoffs between carriers’ cellular networks and local wireless LANs for mobile users. (With Cisco/Nokia solution, it appears, the user can manually select Wi-Fi or cellular.)
These technical solutions (which relate only to voice, not other communications modes) have been available for years now, although as they are carrier-centric solutions—that is, they are deployed in carrier networks—and the carriers by and large have not seen fit to deploy them yet, they are not currently an option for many would-be adopters.
There are, however, several enterprise-centric solutions that chúng tôi has written about in some detail. Technology from DiVitas Networks, OEM provider FirstHand Technologies, and (to a lesser degree) Siemens Communications, not only goes beyond basic F/MC to mobilize PBX functions and/or other key modes of enterprise communication, they are fully available for deployment now.
For Cisco to get to the place where these providers sit today—again, with a level of functionality that could be reasonably termed mobile unified communications—will not be easy.
Providing extended communications features, such as PBX functions (hold, forward, extension dialing, etc.), e-mail, conferencing, corporate directory access, and the like over cellular networks is not a trivial problem.
Doing this over Wi-Fi is relatively easy, DiVitas CEO Vivek Khuller told chúng tôi in a recent conversation, “because you are in control of the network and it’s an all-IP network. However to provide the same feature set over cellular is not a trivial task. That requires coordination—both on the client side and on the server side—between two disparate networks: cell voice and cell data,” Khuller said.
“When you combine all three together—cell voice, cell data, and Wi-Fi—it gets even more difficult,” Khuller continued. “There could be three people on a single call; one on cell, one on campus Wi-Fi, another on public Wi-Fi—three very different networks, controlled by three separate entities. How do you now manage that call—without echo, latency, with everybody having equal features?”
From DiVitas’s perspective, the task is far easier if that functionality was a fundamental goal of the product’s initial design—from the ground up. It’s tougher to do as an add-on to an architecture that didn’t envision it at the outset.
Which brings us to Cisco’s second announcement (last week), of the Cisco 3300 Series Mobility Services Engine or MSE (read the release here).
If you don’t know what a Mobility Services Engines is, don’t feel bad. Neither did we. If we’ve got it right, this is an appliance-based middleware platform that will serve the ambitious goal of normalizing and integrating the entire spectrum of networking technologies, both wired and wireless, allowing the sharing of both data and application functionality among devices, regardless of their network connections.
The “platform offers an open application programming interface (API) for consolidating and supporting an array of mobility services across wireless and wired network,” according to the company. That is, software applications—and other appliances—will be able to access resources provided by the MSE.
Cisco will be releasing an initial four software offerings for the MSE platform, one of which—Cisco Mobile Intelligent Roaming (MIR), due out some time in the second half of the year—can facilitate (but not actually execute) handoffs when devices roam between networks.
“If we know that network performance is changing in a way that impacts the application, it might make sense to transition to another network. MIR can provide that intelligence to other platforms that actually trigger the roam,” Chris Kozup, Senior Manager, Cisco Mobility Solutions told our sister publication Wi-Fi chúng tôi in an interview.
Actually bringing about the connection transfer requires another device or gateway, and one member of Cisco’s technology “partner ecosystem”—Silicon Valley startup Agito Networks—announced (in conjunction with the MSE release) that its RoamAnywhere Mobility Router will integrate with the Cisco Engine to provide customers with a full-blown solution for seamless cellular/Wi-Fi handoff.
So, before the end of this year, Cisco VoIP shops will have the tools needed to begin to provide communications capabilities to far-flung mobile workers. For better or worse, it will involve one or more additional devices in the network infrastructure that customers will have to manage and troubleshoot.
This article was first published on chúng tôi
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