Recently, Apple researchers, including C. V. Krishnakumar Iyer, Feili Hou, Henry Wang, Yonghong Wang, Kay Oh, Swetava Ganguli, Vipul Pandey, have developed Trinity, a no-code AI platform for complex spatial datasets. 

The platform enables machine learning researchers and non-technical geospatial specialists to experiment with domain-specific signals and datasets to solve various challenges. It tailors complex Spatio-temporal datasets to fit standard deep learning models–in this case, Convolutional Neural Networks (CNNs), and formulate disparate problems in a standard way, eg. semantic segmentation.

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Recently, Apple researchers, including C. V. Krishnakumar Iyer, Feili Hou, Henry Wang, Yonghong Wang, Kay Oh, Swetava Ganguli, Vipul Pandey, have developed Trinity, a no-code AI platform for complex spatial datasets. 

The platform enables machine learning researchers and non-technical geospatial specialists to experiment with domain-specific signals and datasets to solve various challenges. It tailors complex Spatio-temporal datasets to fit standard deep learning models–in this case, Convolutional Neural Networks (CNNs), and formulate disparate problems in a standard way, eg. semantic segmentation.

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“It creates a shared vocabulary leading to better collaboration among domain experts, machine learning researchers, data scientists, and engineers. Currently, the focus is on semantic segmentation, but it is easily extendable to other techniques such as classification, regression, and instance segmentation,” as per the paper.

Challenges

With the increase in smart devices, a high volume of data containing geo-referenced information is generated and captured. ML techniques have now entered the geospatial domain, including hyperspectral image analysis, high-resolution satellite image interpretation. However, deploying such solutions is still limited due to specific challenges, such as:

• Processing large volumes of Spatio-temporal information and applying ML solutions involves specialised skills and hence has a high barrier of entry, preventing non-technical domain specialists from solving problems on their own.
• The solution differs as data from residential areas will be very different from commercial ones, giving rise to non-standard preprocessing, post-processing, model deployment, and maintenance workflows.
• Engineers process data while scientists run experiments for different problems and involve a lot of back and forth. This hampers the ability to collaborate.

Trinity tackle these challenges by: 

• Bringing information in disparate Spatio-temporal datasets to a standard format by applying complex data transformations upstream. 
• Standardising the technique of solving disparate-looking problems to avoid heterogeneous solutions.
• Providing an easy-to-use code-free environment for rapid experimentation, thereby lowering the bar for entry.

It enables quick prototyping, rapid experimentation and reduces the time to production by standardizing model building and deployment.

The post Now Apple Introduces A No-Code AI Platform appeared first on Artificial Intelligence.

Apple might be working on launching its own search engine and according to reports there are several clues online that support the possibility including job announcements for search engineers. Apple is looking to create its Spotlight Search to take on Google Search with iOS 14 beta.

Reports have it that Google pays billions of dollars to Apple to remain their default search engine on iOS, macOS and iPadOS. And this deal has allegedly also come under the scrutiny of UK market regulators. And possibly because of that, Apple might be looking to develop its own search engine.

As per Coywolf, the several indicators of Apple working on its own search engine include these job postings doe search engineers. The listings stress on integrating artificial intelligence (AI), natural language processing (NLP) and machine learning (ML) into other services.

The new post also adds that with iOS 14 beta and iPadOS 14 beta, Apple’s Spotlight Search bypasses Google Search to show search results. Applebot, which is the tech giant’s won Web crawler has been crawling sites regularly as well. The Applebot support page was also recently updated.

Going by the Spotlight Search behaviour the job postings, there are speculations that Apple’s search engine might actually be a personalised data hub. Coywolf suggests that it might be similar to Google Assistant on Android devices but devoid of ads and completely private.

Apple could put ML and AI to optimal use to return search results based in the user’s contacts, events, emails, files, messages, documents, maps, music, news, notes, photos, reminders, movies and TV shows, third-party apps etc. There are parallel speculations that Apple’s new search engine, if rumours are indeed true, is going to challenge Google’s monopoly on search and impact its ad revenue and data mining.

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Technology won’t stop innovating, regardless of the economic turbulence ahead, and that’s good news for Internet of Things (IoT) stocks.

If anything, the lockdowns put in place by many countries in the response to the novel coronavirus has only accelerated the importance of technology solutions in many sectors. And even as countries re-open their economies, consumers and businesses alike will hesitate to return to their pre-pandemic purchasing habits.

People will rely more on technology in their everyday lives. Innovative solutions utilizing IoT will rise in importance.

These 7 Internet of Things stocks are set for serious growth in the coming months:

• NXP Semiconductors (NASDAQ:NXPI)
• Apple (NASDAQ:AAPL)
• Intel (NASDAQ:INTC)
• Qualcomm (NASDAQ:QCOM)
• Analog Devices (NASDAQ:ADI)
• Johnson Controls (NYSE:JCI)
• Rockwell Automation (NYSE:ROK)

While Covid-19 might be the direct catalyst for these stocks at the moment, investors ought to remember that secular trends in cloud computing and connected devices were already heading this direction. The pandemic has simply moved up the timetable.

Internet of Things Stocks to Buy: NXP Semiconductors (NXPI)

NXP Semiconductors reported first-quarter revenue of $2 billion.  Industrial and IoT contributed to $376 million in revenue, while Automotive added the most at $994 million in the first quarter. The company forecasts Industrial and IoT revenue growing in the low-single-digits.

China’s return to business will lead to the IoT sell-through. Executive Rick Clemmer said China reapproaching normalcy, “gives [the company] confidence in being able to achieve the 6% sequential growth in the industrial & IoT.“

In automotive, demand for radar and battery management will continue. But NXP has more crossover with IoT and Industrial due to the need for connectivity solutions. The company bought Marvell’s (NASDAQ: MRVL) Wi-Fi and Bluetooth connectivity assets in Dec. 2019.

Looking at valuation, NXP stock compares unfavorably with the S&P 500:

Data Courtesy of Stock Rover

Notice from the table above that the company’s gross margin is above both the industry average and the index. As IoT becomes a bigger contributor to revenue, profits will increase, too. Based on the 20 analysts covering NXPI, the average price target is ~$110 (per Tipranks).

Apple (AAPL)

Apple Watch represents a core IoT play for the smartphone giant. The company’s latest iteration of the wearable device adds many useful features that fit with the IoT innovation.

Apple highlighted the ECG app on the Apple Watch “used to facilitate remote ECG measurements and recordings for telemedicine usage, reducing patient and healthcare provider contact and exposure.”

The business is so big that it is now the size of a Fortune 140 company. Future releases of the device will add more features. For example, the ECG app signals the company’s interest in developing more health-related monitoring. Eventually, it may further integrate patient health data and send it to hospitals. This will give Watch wearers better health care, as its providers may monitor them in a more timely fashion.

More recently, Apple and Google (NASDAQ:GOOG) updated the device operating system to support Covid-19 based tracking. The Application Programming Interface will allow the system to track people, using Bluetooth, who may have been close to infected individuals.

On Wall Street, analysts think the stock already trades at fair value. Conversely, Apple has a high (operating) efficiency score of 99 and a momentum score of 96 on Stock Rover.

The momentum is mainly about the stock’s uptrend.

Intel (INTC)

Intel announced the purchase of Rivet Networks on May 20, 2020. This will enhance INTC’s secure Wi-Fi connectivity solutions and let it add “more connected devices and higher bandwidth applications for gaming, video streaming and content creation, as well as for processing increasingly larger file sizes.”

Having lower latency network interface cards is an important aspect of IoT. Still, the Internet of Things Group (IoTG) underperformed in the first quarter. Intel said that operating income dropped 3% due to lower revenue from industrial and retail.

So, as IoTG lags in the near-term, investors may hold INTC stock for the strong Mobileye unit’s performance. Mobileye reported revenue growth of 22% and operating income growing by 29% year-over-year.

Investors may also look forward to Intel’s 10-nanometer-based solutions. Its Snow Ridge processor adds to its portfolio of 5G capabilities. And since the cloud and network infrastructure for 5G are growing faster than seasonal trends, Intel stock will benefit from revenue growth in segments outside of IoTG.

Qualcomm (QCOM)

In the last quarter, Qualcomm reported increased demand for connectivity due to the work-from-home environment. This trend should continue in the months ahead because more workers will be connected to their offices remotely.

QCOM stock still trades at a fair discount of 14 times forward price-to-earnings ratio. The stock market is still undervaluing the company’s future IoT solutions. For example, Qualcomm Technologies helps its customers commercialize their products using its platforms. This includes smart bodies, smart cities, and smart homes.

The company counts on NFC, Bluetooth, Wi-Fi, LTE modems, and Snapdragon processors as some of its IoT solution offerings.

Analog Devices (ADI)

Analog Devices describes itself as the pioneer of advancing solutions that “sense, measure, interpret, connect and analyze.” So, it optimizes data quality and analytics. That way, data is relevant, accurate, and complete. To foster its investments, Analog Devices partners with its customers to design solutions. As a result, the partners solve tough IoT challenges in their applications.

In the second quarter, ADI reported revenue of $1.32 billion and EPS of $1.08. It spent 19% of its revenue on research and development. 95% of its resources went to B2B market opportunities. Expect more solutions and new technologies emerging as a result of more customer engagements during the quarter.

Investors may assume the following metrics below in a 10-year discounted cash flow EBITDA model.

Data courtesy of finbox

Assuming revenue falls this year due to Covid-19 but recovering in the following years, ADI stock has a fair value of around $117.

Johnson Controls (JCI)

Johnson Controls won the “Industrial IoT Innovator of the Year” last year. Through its Johnson Controls Digital Solutions, it is “working with its customers to pull insights that allow for smarter buildings, increased efficiencies and new business value.”

By helping companies innovate and save money by operating more efficiently, the company is a conservative IoT play. More specifically, its connected equipment and services utilize machine learning and predictive diagnostics to increase reliability. Johnson has over 1,600 customers connected across 5,000 buildings and 75,000 equipment and systems. All of these are on an IoT connected platform and solution.

In the second quarter, Johnson reported cash of over $1 billion. Net debt rose $900 million due to share buybacks. The company is positioned to lower its net debt-to-EBITDA leverage to 2 times. EBIT and margins were flat from last year at 8.1% (from the presentation).

As IoT plays a bigger roll in Johnson’s offerings, profitability will climb. On Wall Street, the average price target on JCI stock is ~$33.00.

Rockwell Automation (ROK)

Rockwell Automation is hovering at yearly highs because it continues to differentiate itself from the marketplace. Customers are noticing their IoT offerings. In the second quarter, Compass Intelligence rewarded the company with the Industrial IoT Company of the Year. Gartner’s annual Magic Quadrant survey listed it as a leader in IoT software.

In the second quarter, Rockwell enjoyed strong margins in its Architecture and Software segment. Its solutions and services businesses reported a 1.10 book-to-bill ratio.  The company ended the quarter with $640 million in cash and had a total debt of $2.1 billion. Its net debt to adjusted EBITDA ratio was 1.0. Rockwell’s strong liquidity suggests that it has the flexibility to cut capital expenditures to align with lower revenue in the near-term. Rockwell also cut costs and expects to save over $150 million for fiscal 2020.

ROK stock may not have any upside, as analysts have an average target price target of $183. Still, the IoT drive should result in long-term growth in the medium term.

The post 7 Internet of Things Stocks for Investing in Innovation appeared first on Artificial Intelligence.

Tech giants Apple and Google, in an attempt to combat the spread of the deadly pandemic coronavirus, are working on a contract-tracing tool.

Stronger privacy protections are also on the feature cards as the upcoming Covid-19 contract-tracing tool’s developer version will be launched soon.

How will Covid-19 contract-tracing tool work?

Apple, in a ‘Frequently Asked Questions’ document, explains how the tool will help the authorities and governments around the world in contract-tracing efforts to combat Covid-19.

The tool, according to the FAQ, is a “two-phase exposure notification solution.”

The tool uses Bluetooth technology on respective smartphone which once enabled, will let the user’s device send out beacons that includes a string of random numbers.

The numbers have nothing to do with the users’ identity, and will change every 10-20 minutes for security reasons.

All the beacons will be collected in the system as a list after the tracks on every phone perform this function.

“At least once per day, the system will download a list of beacons that have been verified as belonging to people confirmed as positive for COVID-19 from the relevant public health authority. Each device will check the list of beacons it has recorded against the list downloaded from the server. If there is a match between the beacons stored on the device and the positive diagnosis list, the user may be notified and advised on steps to take next,” the FAQ read.

How will the tool on your device?

First, the users will be required to download the public health apps issued by their country’s government.

After installing the apps, open and accept all the terms and condition before the program is activated.

The technology becomes functional ones you agree to the terms and conditions.

However, to use the technology or not rests with you.

“The choice to use this technology rests with the user, and he or she can turn it off at anytime by uninstalling the contact tracing application or turning off exposure notification in Settings,” the document states.

While this was the first phase of the tool, in the second phase, which will be available in the months to come, the technology will be available at the operating system level.

However, an app will not be required in the second phase of the contract-tracing technology.

The document states: “If a match is detected, the user will be notified, and if the user has not already downloaded an official app they will be prompted to download an official app and advised on next steps. Only public health authorities will have access to this technology and their apps must meet specific criteria around privacy, security, and data control.”

The post Apple and Google to launch COVID-19 contract-tracing tool appeared first on Artificial Intelligence.

Artificial intelligence (AI) is competent to have a revolutionary impact on businesses and consumers globally. It is no longer merely about codifying business logic, Instead, more about making tasks effortless, innovative and removing drudgery from human’s life.

Very few subjects in science and technology have caused much excitement right now as artificial intelligence as some of the world’s brightest minds have said that it’s potential to revolutionise all aspects of our lives.

AI makes it practical for machines to understand from experience, act human-like jobs, and adapt to the latest inputs. The concept works by amalgamating enormous data with quick, smart algorithms, and iterative processing, enabling the software to decipher by analysing patterns in the data in an automatic way.

There is science and well thought algorithm behind all the artificial solutions, where you need to set up proper expectations and clarification to avoid any rumours and myths around the outputs.

While the notion of AI is turning into a massive component of business and consumer transformations, its execution is generally stagnated because of some misconceptions associated with it.

Myth 1: AI will deliver magical results…immediately

The path to AI success is hard and takes time, and not just because of the technology. You also need a strategic framework and an iterative approach to avoid delivering a random set of disconnected AI solutions. The temptation is to go for moonshots to deliver the magic, but such projects often fail to live up to expectations because you don’t have the basics homework done.

AI is not a magic, it requires rigour, logical thinking and long term strategy with a patience to do multiple iteration to get to the result.

Myth 2: AI Will Replace Human Jobs

Most of the times, management look at AI solutions to replace human and reduce the operational cost, creating a sense of fear among the employees.

So, if you think that AI solutions might strip human from their jobs, then you are undeniably wrong.

Reality is, AI and human need each other. AI is at its most valuable when it augments people’s capabilities. It can remove the duplicate work, freeing people up for more strategic activities. That has the added benefit of making people more motivated, productive, and loyal. Enterprise AI also relies on people to feed it the right data and work with it the right way. Often, AI doesn’t provide conclusive answers to issues, but rather highly informed recommendations that an actual human can weigh to make the final decision.

Myth 3: AI Implementation Needs Huge Investment

Artificial development’s resolutions appear to be tremendously scientific and complicated. This inclination recommends that just a modern tech organisation, including Google, Amazon, or Apple, with an extended team of experts and billion-dollar budgets can pay for implementing AI. In reality, there are a lot of smart tools existing for an enormous variety of organisation, which can be utilised to implement AI in their business procedures.

Myth 4: AI Algorithms are Competent to Process Any Data

Most of you must believe that ML algorithms are one of the most crucial elements in the entire system. An algorithm might appear to be robust and linked with the human brain, which can make intellect of any untidy data.

It is not possible, for algorithms, to make decisions without human intervention as they don’t have magic power. It requires a specific piece of data to get impeccable results.

Myth 5: AI will Conquer Humanity

Machines are powerless to imagine similar to people and will barely be taught to do so. In fact, computers are going to have an optimistic impact on the world by supporting people in a lot of fields, building innovative business models, communities, and skills. It’s certainly true that the advent of AI and automation has the potential to seriously disrupt labour – and in many situations it is already doing just that. However, seeing this as a straightforward transfer of labour from humans to machines is a vast over-simplification. In fact, a lot of AI focus has been on reducing the “drudgery” of day-to-day aspects of the work. AI gives an opportunity to upgrade your skills and move up in your career ladder at the same time.

About the Author: A technology and product leader, Rahul Kumar is Group Chief Product Officer with HT Media Group. An alumni of BIT Mesra, who later on honed his technology management skills from IIT Delhi, has been leveraging AI, ML and IOT to solve business and consumer problems across technology led startups and conglomerate.

The post Artificial Intelligence Myths: Reality check appeared first on Artificial Intelligence.

It’s no secret that Apple (NASDAQ: AAPL) has spent a great deal of time, money, and resources to make its devices — particularly the iPhone — smarter using artificial intelligence (AI). One of the factors that differentiates Apple from the other companies developing the technology is its consistent commitment to data privacy. To advance that agenda, Apple has focused on on-device AI rather than taking a more cloud-centric approach, which helps explain the company’s latest acquisition.

Apple has reportedly acquired AI start-up Xnor.ai for about $200 million, according to a report from GeekWire, citing “sources with knowledge of the deal.” Xnor specializes in developing complex AI systems that are extremely efficient, able to use minimal amounts of power and run locally on devices like smartphones rather than by transmitting information to remote data centers. The company has also created algorithms that specialize in capturing and processing images on small devices, a computing-intensive task that has historically been accomplished in the cloud.

Cutting-edge AI tech

The technology developed by Xnor has already been used in a variety of applications. It was integrated by smart camera company Wyze in its “person detection” feature before being unceremoniously removed recently “due to the unexpected termination of our agreement with our AI provider,” undoubtedly the result of Xnor being acquired by Apple. The company is also developing an application that would help improve inventory management by monitoring shelves in retail stores. Previous advances include the development of an AI chip that can run for years using a tiny battery or even a small solar-powered cell.

The technology Apple is gaining could be used to improve future versions of the iPhone by augmenting its existing camera capabilities, or it could be used by developers to create apps as part of the company’s Core ML (machine learning) toolkit. It also continues a multiyear push by Apple to run AI on its devices as a way to help ensure the privacy of its users.

The cloud versus the edge

Deep learning, a branch of AI research, involves amassing huge amounts of data and collecting it in a central location in the cloud for processing. Historically, this has been necessary because the process is computationally intensive and requires complex calculations, and the sheer volume of data can be enormous. Apple’s stance on data privacy is well documented, one the company wasn’t willing to forego in order to keep up with its big tech rivals in the race for AI.

As a result, Apple has taken a different path. Over the years, the company has focused more on developing the next generation of AI systems that rely more on using minimal information and processing the data on the device itself, called at-the-edge or simply edge computing, rather than sending user information to the cloud.

It’s still necessary for Apple to process some of its data in the cloud, but the company obscures personally identifiable information using a process known as differential privacy. In order to protect user information, the technology injects “noise” or random data into the information it collects before sending it to Apple’s servers, which helps anonymize the data and protect the user’s identity.

Not the first time

This isn’t the first such small AI-focused company Apple has acquired. In 2016, the company acquired AI start-up Turi, a company that created AI-based tools for app developers that could scale to a large number of users. This became an integral part of Apple’s Core ML app developer frameworks.

Find out why Apple is one of the 10 best stocks to buy now

Apple will no doubt continue to acquire smaller AI companies to augment its internal AI efforts, particularly when it comes to the area of data privacy, and we’ll no doubt see the integration of this technology into Apple’s products in the coming months and years.

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The post Here’s Why Apple Paid $200 Million for an AI Startup appeared first on Artificial Intelligence.

Apple has struggled in recent years to establish a robust artificial intelligence (A.I.) practice. This partially stems from the company’s ironclad privacy policies—it’s more difficult to analyze datasets for insights when internal rules prevent the company from using every piece of user data it can vacuum up. Nonetheless, Apple’s newest projects show that it’s powering ahead anyway—including one platform that, if it’s ever released, could change how you use A.I. and machine learning (ML).

(It’s worth remembering how, in a 2015 speech, Apple CEO Tim Cook accused tech giants such as Facebook and Google of “gobbling up everything they can learn about you and trying to monetize it,” which he framed as “wrong.” It seems unlikely that Apple’s stance on data and privacy will change during Cook’s tenure.)

According to a just-released paper with the dry-but-mysteriously-compelling title “Overton: A Data System for Monitoring and Improving Machine Learned Products,” a group of Apple researchers describe their work on a machine-learning platform (named—you guessed it—“Overton”) designed to “support engineers in building, monitoring, and improving production machine learning systems.”

How does Overton go about this herculean task? By automating the nitty-gritty of machine-learning model construction, deployment, and monitoring. Apple claims that the platform is already in use, supporting multiple initiatives “in both near-real-time applications and back-of-house processing.” These Overton-powered applications have “answered billions of queries in multiple languages and processed trillions of records reducing errors 1.7 – 2.9x versus production systems.”

This means that any researcher or engineer working with Overton will need to trust that the platform can recognize and fix issues with a model; otherwise they’ll presumably need to dig into the algorithms and datasets themselves, a lengthy and stressful process. But if it truly works as it says on the proverbial tin, it should reduce the time necessary to churn out results. Here’s an except from the paper on what the model inputs:

Overton takes as input a schema whose design goal is to support rich applications from modeling to automatic deployment. In more detail, the schema has two elements: (1) data payloads similar to a relational schema, which describe the input data, and (2) model tasks, which describe the tasks that need to be accomplished. The schema defines the input, output, and coarse-grained data flow of a deep learning model. Informally, the schema defines what the model computes but not how the model computes it: Overton does not prescribe architectural details of the underlying model (e.g., Overton is free to embed sentences using an LSTM or a Transformer) or hyperparameters, like hidden state size.

And this is what Overton does with that schema/input:

Given a schema and a data file, Overton is responsible to instantiate and train a model, combine supervision, select the model’s hyperparameters, and produce a production-ready binary. Overton compiles the schema into a (parameterized) TensorFlow or PyTorch program, and performs an architecture and hyperparameter search. A benefit of this compilation approach is that Overton can use standard toolkits to monitor training (TensorBoard equivalents) and to meet service-level agreements (Profilers). The models and metadata are written to an S3-like data store that is accessible from the production infrastructure. This has enabled model retraining and deployment to be nearly automatic, allowing teams to ship products more quickly.

So Overton is going to reduce the amount of coding that machine-learning researchers and data scientists need to do—allowing them to observe and manage the process from a higher level. Plus, it’s interoperable with platforms such as Google’s TensorFlow, which are becoming industry-standard.

Apple isn’t unique in producing a tool that attempts to take as much of the coding grind out of the machine-learning process as possible. For example, Google has AutoML, which is similarly designed to produce working machine-learning models with a minimum of code; there’s also Microsoft’s Machine Learning Studio, which attempts to boil down ML model building to a drag-and-drop process. Automating ML and A.I. is key to these technologies going as mainstream as possible.

The revelation of Overton is also interesting, as it shows that Apple’s researchers are moving on parallel tracks to other tech firms. Apple’s tool might help its internal staffers catch up to their rivals in A.I./ML, but it’s an open question whether they’ll ever transform it into a public-facing product, just as they’ve done for CoreML and other tools.

The post Apple ‘Overton’: Automating Low-Code Machine Learning appeared first on Artificial Intelligence.

Livongo Members can now connect their smartwatches to the Livongo mobile app to receive real-time Livongo notifications on the surface of their choice enabling Livongo to provide more timely and relevant information to Members and their care teams.

Smartwatches are one of the fastest growing new technologies today and it is estimated that one in six people in the United States own a smartwatch1. These new integrations allow Livongo to offer Members behavioral Health Nudges and health information directly to their smartwatch. In addition, Livongo now offers Members the ability to sync their steps data from their Apple, Fitbit, or Samsung smartwatches with their Livongo app. This data enables Livongo’s AI+AI engine to provide more personalized activity-related insights.

Livongo’s smartwatch integration is the latest example of how the company is applying innovative technology to the Livongo Member experience. In February, Livongo announced that it will leverage Amazon Lex and Amazon Polly to power its voice-enabled cellular blood pressure monitoring system. Two months later, Livongo announced a collaboration with Amazon Alexa to offer its Members the ability to ask any of their Alexa-enabled devices to provide their blood glucose readings and health tips via the new HIPAA-compliant Livongo skill.

Our smartwatch integration allows us to capture information from our Members, add it to our AI+AI engine, and return actionable, personalized, and timely information back to them, said Dr. Jennifer Schneider, M.D., M.S., President of Livongo. By offering another way to access personalized health insights, we are able to more easily influence positive behavior change, which we know can lead to better health.

As part of the new smartwatch integration, Livongo now offers notifications for interactive challenges. These challenges are designed to empower Members to form healthy lifestyle habits that support their diabetes management and other chronic conditions in areas such as nutrition, exercise, sleep, stress management, and more. Each challenge lasts five days and is designed to enable sustainable lifestyle behavior changes that are known to drive measurable clinical outcomes by providing Members with specific and achievable goals, tools for tracking progress, and daily educational content. Examples of current challenges include incorporating walking into a daily routine or replacing sugary beverages with water. Livongo is able to anticipate potential barriers that Members might face during their behavior change journey and uses educational content to proactively address those barriers.

We are excited to offer our Members the opportunity to conveniently access valuable health information and Health Nudges using their existing smartwatch devices, said Livongo Chief Product Officer Amar Kendale. As we continue to expand our Applied Health Signals platform, we can use the integration to aggregate more important health data that we can then interpret to better understand the unique needs of our Members.

The post Livongo Integrates with Leading Smartwatches; Delivers Personalized Health Insights to People with Chronic Conditions appeared first on Artificial Intelligence.

Source – washingtonpost.com

Artificial intelligence is the future. Google, Microsoft, Amazon and Apple are all making big bets on AI. (Amazon owner Jeff Bezos also owns The Washington Post.) Congress has held hearings and even formed a bipartisan Artificial Intelligence Caucus. From health care to transportation to national security, AI has the potential to improve lives. But it comes with fears about economic disruption and a brewing “AI arms race .” Like any transformational change, it’s complicated. Perhaps the biggest AI myth is that we can be confident about its future effects. Here are five others.

It is certainly true that conversations with AI chatbots are often unintentionally funny. And no one who interacts with Alexa or Siri or Cortana is going to say they pass the Turing Test. “Their responses, often cobbled together out of fragments of stored conversations, make sense at a local level but lack long-term coherence,” Brian Christian wrote in a 2012 Smithsonian Magazine article. Garbled sentences and ridiculous responses often make clear just how poorly machines mimic human capabilities — or even, sometimes, how they process information. “Machines don’t have understanding,” Garry Kasparov told TechCrunch last year. “They don’t recognize strategical patterns. Machines don’t have purpose.”

But AI is already writing financial news, sports stories and weather reports, and readers aren’t noticing. From the Associated Press to The Washington Post, it’s becoming increasingly common. AI is also producing “deep fake” videos — from invented speeches by politicians to pornography featuring celebrities’ computer-generated faces — that many people think are real. These rapid advances present significant concerns, shaking the public’s confidence in what they see and hear. As a 2017 Harvard study warned, “The existence of widespread AI forgery capabilities will erode social trust, as previously reliable evidence becomes highly uncertain.”

China’s national strategy to lead the world in artificial intelligence — which calls for “the training and gathering of high-end AI talent” — has elicited fear and loathing in the United States. “China’s prowess in the field will help fortify its position as the dominant economic power in the world,” Will Knight observed in MIT Technology review in 2017. Writing in the Hill, Tom Daschle and David Bier warned in January that “the U.S. government is behind the curve.”

While there is clearly reason for concern about the United States’ standing, China’s strategic document admits that “there is still a gap between China’s overall level of development of AI relative to that of developed countries.” According to Jeffrey Ding , a University of Oxford researcher, “China trails the U.S. in every driver except for access to data.” The United States also has more AI experts, who publish more Association for the Advancement of Artificial Intelligence papers on the topic, and far more commercial investments in the field.

That said, given China’s dedication to pursuing AI, the United States will need to take a concerted societal approach if it wants to maintain its dominant position. Such efforts are already underway: In March, the New York Times reported that the Pentagon is attempting to work with Silicon Valley companies to push projects ahead.

As early as 1964, a group of Nobel Prize winners known as the Ad Hoc Committee on the Triple Revolution warned that machines would usher in “a system of almost unlimited productive capacity” that would cause disruptive levels of unemployment. More recently, a Mother Jones headline proclaimed, “You Will Lose Your Job to a Robot — and Sooner Than You Think.” The article noted that traditional blue-collar and white-collar workers alike may be displaced, leading to joblessness and poverty. Taking up that torch, one truck driver worried in the Guardian that “we will soon be extraneous — roadkill, so to speak, except we won’t be dead.”

But in transforming work, AI may also create new jobs. As Joel Mokyr, an economic historian at Northwestern University, observed, “We can’t predict what jobs will be created in the future, but it’s always been like that.” Historically, technological change has initially diminished, but then later boosted, employment and living standards by enabling new industries and sectors to emerge.

We don’t yet know how AI will affect employment in the long term. Between now and then, there may still be disruptions, and we’ll have to grapple with the growing gap between those who have the skills to thrive in a changing world and those who don’t.

It’s easy to imagine that relying on computers to make critical decisions would take human bias out of the equation. “Humans are hindered by both their unconscious assumptions and their simple inability to process huge amounts of information,” wrote Digitalist Magazine last year. Judges around the United States are using AI tools in sentencing decisions, on the assumption that these systems can offer “the most objective information available to make fair decisions about prisoners.”

If only it were that simple. In one example that shows AI’s vulnerability to bias, ProPublica found that a program intended to play a key role in criminal justice decisions from bail to sentencing was almost twice as likely to rate black defendants as probable repeat offenders than white defendants. The program also incorrectly rated white defendants as low-risk more often than blacks. “It’s often wrong — and biased against blacks,” ProPublica wrote.

In another example, a 2015 Carnegie Mellon University experiment found that far fewer women were being shown online ads for jobs paying more than $200,000 than were men. “Many important decisions about the ads we see are being made by online systems,” said Anupam Datta, associate professor of computer science and electrical and computer engineering at Carnegie Mellon. “Oversight of these ‘black boxes’ is necessary to make sure they don’t compromise our values.” Researchers are already addressing the bias issue, seeking to head off mistakes and build more transparent algorithms.

Some prominent science and technology leaders have raised grave concerns about the implications of AI for humanity’s future. “The danger of AI is much greater than the danger of nuclear warheads by a lot, and nobody would suggest that we allow anyone to build nuclear warheads if they want,” Elon Musk said at the South by Southwest conference in March. “I fear that AI may replace humans altogether,” Stephen Hawking told Wired in 2017.

The truth is we simply don’t know where AI will lead us, but that doesn’t mean murderous terminators are going to start stalking the streets. In a 2015 open letter, experts associated with the nonprofit Future of Life Institute warned against the rise of autonomous weapons systems, which could be abused by ill-intentioned humans. The more pressing concern might not be that AI is a risk to us, but that we’re a risk to ourselves if we don’t exercise caution in how we push ahead with our AI experiments.

In some contexts, AI can save lives. In March, a self-driving car struck and killed a pedestrian in Arizona, an incident that presaged trouble for the emerging technology. Nevertheless, many researchers have long held that self-driving vehicles will help reduce traffic fatalities overall. A 2017 Rand Corp. report, for example, concludes that introducing autonomous automobiles to the streets sooner could prevent hundreds of thousands of deaths.

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The future of all biometrics will involve artificial intelligence. That statement might seem strange. After all, biometrics is a security issue, one that often involves complex algorithms, randomly generated passcodes, and multiple authentication steps.

But in the future, data security will rely on AI and machine learning to analyze data and determine whether someone is trying to gain access illegally.

Case in point: Apple’s new Face ID technology, which will debut on the iPhone X. The device will project up to 30,000 dots on your face to create a 3D map. The data will be processed using a new A11 Bionic chip and, on a new neural engine located on the phone — importantly, not in the cloud — analyzed using machine learning. According to Apple, you can put on a pair of glasses, but the machine learning will be able to determine that it is, in fact, your face. The same AI might also scan a photograph a hacker is holding up to the phone, or even a 3D-printed mask, and compare the results to the data collected from your actual face.

Using this kind of AI for data security isn’t a temporary approach — it’s going to be a major requirement to combat the onslaught of users who are determined to compromise corporate networks. AI will analyze phishing attacks, fend off social engineering ploys, and even fight bot networks that are using ever-more-intricate methods to break into secure environments.

As a side bonus, Face ID is also simple to use: You just look at the phone. There’s no need to press your finger on a Touch ID sensor, no reason to type in a passcode. While a Face ID demo this week during an Apple event announcing the phone appeared to fail, some experts have said it worked as expected because too many people that morning looked at the phone.

That said, you might wonder: Is Face ID the best way to protect an iPhone?

Mike Fumai, the president and COO at security company AppGuard, tells VentureBeat the technology is highly secure. One of the main reasons has to do with the architecture. Face ID does not run in the cloud, and the extra processing power from the neural engine, he says, means Apple was not tempted to make the machine learning less complex as a way to boost performance on the device. He also explained that a fingerprint scan is a 2D render, but a facial scan in 3D includes many additional data points and is more secure.

“Apple is at version 1.0 so it must be easy and consistent in a vast and diverse end-user population,” he says. “[After this], imagine what facial gestures they might include in 2.0 that provide a more complex and dynamic biometric authentication.”

Fumai says the neural engine will only help improve security with each new release.

That said, there are concerns.

Stephen Maloney, the EVP of business development and strategy at security firm Acuant, says the facial recognition is a step in the right direction, but there are some interesting workarounds. Maloney says the biggest concern is that iPhone users will rely on Face ID as a single form of authentication because it’s so easy — just look at the phone and you’re authenticated. With Touch ID, some users decided to use the fingerprint reader, but then also added a passcode.

Maloney also explained that Face ID lacks what he calls active intent. With two-factor authentication, the user has to intentionally scan a finger and type a code — the user has to participate in the authentication. With Face ID, it’s possible a teenager could break into their parent’s phone by scanning Mom or Dad’s face while they’re not paying attention. (Apple states the machine learning knows if you are looking directly at the phone and it won’t work if your eyes are closed.)

The iPhone 7 has a useful security feature: You can press the Home button five times to activate your login. It’s not clear whether the iPhone X will support this feature, but it’s unlikely because, for starters, it doesn’t have a physical Home button. Plus, Face ID is meant to be fluid and easy, so you can authenticate and make purchases quickly, without any other steps. (Fumai says the iPhone X will likely still support this feature or offer some other method to re-activate security.)

Nathan Wenzler, the chief security strategist at AsTech Consulting, says the most important milestone here is the neural engine. It is a sign of things to come, he says, because it means security can become more stable, streamlined, and easy for any user. The power and functionality of the Face ID security runs on the phone itself; the user doesn’t have to remember a complex password, and he or she doesn’t have to scan a finger or an eye.

The reality? On paper, Face ID and its machine learning algorithms look promising. Every expert noted that there is no way to know if the iPhone X is highly secure until everyone — including the hackers and the security professionals — get their hands on the device.

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