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We work at the George W. Bush Institute on challenges that would appear to be disconnected: modernizing Americas immigration system and promoting a reliable flow of information. But they come together directly and forcefully at Americas southern border, which remains the epicenter of the nations immigration debate and has emerged as a new front line in the need for truth-telling over fake news.

We see border issues and disinformation converging in three ways: smugglers spreading false information about border security, those same smugglers spreading disinformation about the safety of trips to the border and a Russian campaign to spread misleading narratives. Combating these realities will require comprehensive immigration reform, a vigilant effort to counter disinformation and better use of Spanish-language media to convey truthful information.

Of course, human smugglers have long used lies to tempt migrants to come to the United States border. They continue that habit today by leading would-be migrants from El Salvador, Guatemala and Honduras to believe that the Biden administration is throwing the gates open for them to seek a new life in the United States. As NPR reported this spring, Misinformation spread by smuggling organizations is helping spur this surge in migration from Central America.

The border, however, is not wide open. While President Biden rescinded the Trump administration policy of not allowing in unaccompanied minors or some asylum-seekers with children, the current administration is drawing upon public health guidance as it continues to expel migrants along the southern border. According to U.S. Customs and Border Protection, 103,014 of the 178,416 migrants who Border Patrol encountered in June were sent back to Mexico.

Smugglers also maliciously trick migrant families into thinking that their trip to the United States will be an easy, comfortable family vacation. In reality, crossings at times run from brutal to deadly.

The Russians, meanwhile, are adding an extra wrinkle. Adm. Craig Faller, head of the U.S. Southern Command, recently informed Congress that disinformation can drive migration. As an example, pieces from RT en Espaol, which is part of the government-controlled Russia Today operation, end up in popular local media outlets, where they provide misleading information about the border to potential migrants.

The best way to stop the unreliable flow of information is to fix our broken immigration system, which gives smugglers ample room to spread disinformation. If we modernize our system with regularized, legal and realistic pathways for immigrants to enter our country, we could help curb inaccurate information. We particularly should expand temporary worker visa programs and diversify employment-based green cards so that migrants who want to work here can do so without attempting to cross the southwest border.

Countering disinformation also involves a smart media strategy. Advertising in Central American media outlets is crucial to countering the smugglers misinformation as well as misleading RT en Espaol stories. Earlier this year, the White House launched an ad campaign in Spanish- and Indigenous-language media outlets to inform would-be migrants our border is not wide-open. Good. Keep up the just-the-facts campaign.

For their part, U.S. journalists should remain vigilant about distinguishing between asylum-seekers and other migrants. U.S. law stipulates that seeking asylum is the legal right of people trying to escape corruption, violence and extortion at home. TV footage of people crossing on foot, especially, should provide context about who is attempting to cross the border legally and illegally. Otherwise, the footage risks sensationalizing immigration realities.

Spanish-language media outlets in the United States are an important means for the administration as well as journalists to get the facts out, and they should take that responsibility seriously. Spanish-language papers in Los Angeles, New York, Dallas and elsewhere are major sources of information for families here and abroad.

Spanish-language TV stations are especially important. The Pew Research Centers latest data show that about 1.1 million viewers watch Univisions evening newscast, while about 700,000 people view Telemundos evening newscast. Those networks also own affiliates that reach local audiences with their own reporting.

We can and must solve our immigration challenges, starting with reforming our immigration system. Solving that challenge, however, also requires a reliable flow of truthful information.

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Commentary: Getting information right is crucial to solving the border crisis - Press Herald

Companies widely expect that artificial intelligence (A.I.) and machine learning will fundamentally change their operations in coming years. To hear executives talk about it, apps will grow smarter, tech stacks will automatically adapt to vulnerabilities, and processes throughout organizations will become entirely automated.

Given the buzz around A.I., its easy for predictions to easily slip into the realm of the fantastical (In less than six months, well have cars that drive themselves! And apps that predict what a user wants before they want it!). Its worth taking a moment to see what companies areactuallydoing with A.I. at this juncture.

To that end, CompTIArecently asked 400 companiesabout their most common use-cases for A.I. Heres what they said:

The pandemic has accelerated digital transformation and changed how we work, Khali Henderson, Senior Partner at BuzzTheory and vice chair of CompTIAs Emerging Technology Community, wrote in a statement accompanying the data.We learnedsomewhat painfullythat traditional tech infrastructure doesnt provide the agility, scalability and resilience we now require. Going forward, organizations will invest in technologies and services that power digital work, automation and human-machine collaboration. Emerging technologies like AI and IoT will be a big part of that investment, which IDC pegs at $656 billion globally this year.

That predictive sales/lead scoring would top this list makes a lot of senseif companies are going to invest in A.I., theyre likely to start with a process that can provide a rapid return on investment (and generate a lot of cash).According to CompTIA, A.I. helps with more effective prioritization of sales prospects via lead scoring and provides detailed, real-time analytics. Its a similar story with CRM/service delivery optimization, where A.I. can help salespeople and technologists better identify potential customers and cross-selling opportunities.

Companies have spent years working on chatbots and digital assistants, hoping that automated systems can replace massive, human-powered call centers. So far, theyve had mixed results;the early generations of chatbotswere capable of conducting simple interactions with customers, but had a hard time with complex requests and the nuances of language. The emergence of more sophisticated systems likeGoogle Duplexpromises a future in which machines effectively chat with customers on a range of issuesprovided customers can trust interacting with software in place of a human being.

As A.I. and machine learning gradually evolve, opportunities to work with the technology will increase. While many technologists tend to equate artificial intelligence withcutting-edge projectssuch as self-driving cars, this CompTIA data makes it clear that companies first use of A.I. and machine learning will probably involve sales and customer service. Be prepared.

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Here's How Companies are Using AI, Machine Learning - Dice Insights

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Artificial Intelligence and machine learning are seen as pillars of the next generation of technological advancement in broadcast media for a variety of reasons, including the ability to sift through mountains of data while identifying anomalies, spotting trends and alerting users to potential problems before they occur without the need for human intervention. With the more data they ingest these models improve over time, meaning the more ML models utilized across a variety of applications, the faster and more complex the insights derived from these tools become.

But to truly understand why machine learning provides enormous value for broadcasters, lets break it down into use cases and components within broadcast media where AI and ML can have the greatest impact.

Imagine a live sporting event stopsstreaming,or that framesstart dropping for no apparent reason.Viewers are noticing quality problems and starting to complain.Technicians are baffled and customers may have just missed the play of the year. Revenue therefore takes a hit and executives want to know what is to blame.

These are situations every broadcaster wants to avoid, and in these tense moments there is no time to lose viewers are flipping to otherservices andad revenue is being lost by the second. What went wrong? Who or what is to blame and how can we get this back up and running immediately, while mitigating this risk in the future? Modern broadcasters need to know before problems happen not be caught in a crisis trying to pick up the pieces after an incident.

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The promise of our interconnected world means video workflowsareinteracting, intertwining, and integrating in new ways every day, simultaneously increasing information sharing, agility and connectivity while producing increasingly complex challenges and issues to diagnose. As more on-prem and cloud resources are connected with equipment from different vendors, sources, and partner organizationsdistributing to new device types,thereisan enormous, ever-expanding number of log and telemetrydata produced.

As a result, broadcastengineers have more information than they can effectively process. They routinely silence frequent alerts and alarms because with too much data overload it can be impossible to tellwhat isimportant and what is not. This inevitably leaves teams overwhelmed and lacking insights.

Advanced analytics and ML can help with these problems by making sense of overwhelming quantities of data, allowing human operators to sift through insignificant clutter and to focus and understand where issues are likely to occur before failures are noticed. Advanced analytics provide media companies the unprecedented opportunity to leverage sophisticated event correlation, data aggregation, deep learning, and virtually limitless applications to improve broadcast workflows. The benefit is to be able to do more with less, to innovate faster than the competition and prepare for the future both by increasing your knowledge base and opening the potential for cost reduction and time savings, honing in on the crucial details behind the data that matters most to both their users and organization.

One of the biggest challenges facing broadcast operations engineers is to recognize when things are not working before the viewers experience is affected. In a perfect world operators and engineers want to predict outages and identify potential issues ahead of time. Machine learning models can be orchestrated to recognize the normal ranges based on hundreds to thousands of measurements beyond the ability of a human operator and alert the operator in real time when a stream anomaly occurs. While this process normally requires monitoring logs on dozens of machines and keeping track of the performance of network links between multiple locations and partners, using ML allows the system to identify patterns in large data sets and helps operators focus only on workflow anomalies dramatically reducing workload.

Anomaly detection works by building a predictive model of what the next measurements related to a stream will be for example, the round-trip time of packets on the network or the raw bitrate of the stream and then determining how different the expected value is from the next measurement. As a tool to sort through normal and abnormal streams, this can be essential, especially when managing hundreds or thousands of concurrent channels. One benefit of anomalous behavior identification would be enabling an operator to switch to a backup link that uses a different network link before a failure occurs.

Anomaly detection can also be a vital component of reducing needless false alarms and reducing time waste. Functionality such as customizable alerting preferences and aggregated health scores generated by threat-gauging data points assist operators to sift through and assimilate data trends so they can focus where they really need to. In addition, predictive and proactive alerting can be orders of magnitude less expensive and allow broadcasters to be able to identify the root causes of instability and failure faster and easier.

A major challenge to any analytics system is data collection. When you have a video workflow comprised of machines in disparate data centers running different operating systems and tools, it can be difficult to assimilate and standardize reliable, relevant data that can be used in any AI/ML system. While there are natural data aggregation points in most broadcast architectures for example if you are using a cloud operations and remote management platform or common protocol stack this is certainly not a given.

Although standards exist for how video data should be formatted and transmitted, few actually describe how machine data, network measurements, and other telemetry should be collected, transmitted and stored. Therefore it is essential to select a technology partner that sends data to a common aggregation point where it is parsed, normalized and put into a database while supporting multiple protocols to support a robust AI/ML solution.

Once you have a method for collecting real-time measurements from your video workflow, you can feed this data into a ML engine to detect patterns. From there you can train the system not only to understand normal operating behavior for anomaly detection, but also to recognize specific patterns leading up to video degradation events. With these patterns determined you can also identify common metadata related to degradation events across systems, allowing you to identify that the degradation event is related to a particular shared network segment.

For example, if a particular ISP in a particular region continues to experience latency or blackout issues, the system learns to pick up on warning signs ahead of time and notifies the engineer before an outage preventing issues proactively while simultaneously improving root cause identification within your entire ecosystem. Developers can also see that errors are more often observed using common encoder or network hardware settings. Unexpected changes in the structure of the video stream or the encoding quality might also be important signals of impending problems. By observing correlations, ML gives operators key insights into the causes of problems and how to solve them.

Predictive analytics, alerts and correlations are useful for automated failure prediction and alerting, but when all else fails, ML models can also be used to help operators concentrate on areas of concern following an outage, making retrospective analysis much easier and faster via root cause analysis.

With workflows that consist of dozens of machines and network segments, it is inherently difficult to know where to look for problems. However, ML models, as we have seen, provide trend identification and help visualize issues using data aggregation. Even relatively straightforward visualizations of how a stream deviates from the norm are incredibly valuable, whether in the form of historical charts, customizable reports or questions as simple as how a particular stream compares to a similar recent stream.

Leveraging AI and ML to improve operational efficiency and quality provides a powerful advantage while preparing broadcasters for the future of live content delivery over IP. Selecting the right vendor for system monitoring and orchestration that integrates AI and ML capabilities can help your organization make sense of the vast amounts of data being sent across the media supply chain and be a powerful differentiator.

As experiments to test hypotheses are essential to the traditional learning process, the same goes for ML models. Building, training, deploying, and updating ML models are inherently complex, meaning providers in cooperation with their users must continue to iterate, compare results, and adjust accordingly to understand the why behind the data, improving root cause analysis and the customer experience.

Machine learning presents an unprecedented opportunity for sophisticated event correlation, data aggregation, deep learning, and virtually unlimited applications across broadcast media operations as it evolves exponentially year to year. As models become more informed and interconnected, problem solving and resolution technology based on deep learning and AI will become increasingly essential tools. Broadcast organizations looking to prepare themselves for such a future would be wise to prepare for this eventuality by choosing the right vendor to integrate AI and ML enabled tools into their workflows.

Andrew leads Zixis Intelligent Data Platform initiative, bringing AI and ML to live broadcast operations. Before Zixi he led the video platform product team at Brightcove where he spent 6 years working with some of the largest broadcasters and media companies. Particular areas of interest include live streaming, analytics, ad integration, and video players. Andrew has an MBA from Babson College and a BA from Oberlin College.

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Column: Simplifying live broadcast operations using AI and machine learning - NewscastStudio

A new study published in PLOS One has found a relationship between a persons body shape and their family income. The findings provide more evidence for the beauty premium a phenomenon in which people who are physically attractive tend to earn more than their less attractive counterparts.

Researchers have consistently found evidence for the beauty premium. But Suyong Song, an associate professor at The University of Iowa, and his colleagues observed that the measurements used to gauge physical appearance suffered some important limitations.

I have been curious of whether or not there is physical attractiveness premium in labor market outcomes. One of the challenges is how researchers overcome reporting errors in body measures such as height or weight, as most previous studies often defined physical appearance from subjective opinions based on surveys, Song explained.

The other challenge is how to define body shapes from these body measures, as these measures are too simple to provide a complete description of body shapes. In this study, collaborated with one of my coauthors (Stephen Baek at University of Virginia), we use novel data which contains three-dimensional whole-body scans. Using a state-of-the art machine learning technique, called graphical autoencoder, we addressed these concerns.

The researchers used the deep machine learning methods to identify important physical features in whole-body scans of 2,383 individuals from North America.

The data came from the Civilian American and European Surface Anthropometry Resource (CAESAR) project, a study conducted primarily by the U.S. Air Force from 1998 to 2000. The dataset included detailed demographic information, tape measure and caliper body measurements, and digital three-dimensional whole-body scans of participants.

The findings showed that there is a statistically significant relationship between physical appearance and family income and that these associations differ across genders, Song told PsyPost. In particular, the males stature has a positive impact on family income, whereas the females obesity has a negative impact on family income.

The researchers estimated that one centimeter increase in stature (converted in height) is associated with approximately $998 increase in family income for a male who earns $70,000 of the median family income. For women, the researchers estimated that one unit decrease in obesity (converted in BMI) is associated with approximately $934 increase in the family income for a female who earns $70,000 of family income.

The results show that the physical attractiveness premium continues to exist, and the relationship between body shapes and family income is heterogeneous across genders, Song said.

Our findings also highlight importance of correctly measuring body shapes to provide adequate public policies for improving healthcare and mitigating discrimination and bias in the labor market. We suggest that (1) efforts to promote the awareness of such discrimination must occur through workplace ethics/non-discrimination training; and (2) mechanisms to minimize the invasion of bias throughout hiring and promotion processes, such as blind interviews, should be encouraged.

The new study avoids a major limitation of previous research that relied on self-reported attractiveness and body-mass index calculations, which do not distinguish between fat, muscle, or bone mass. But the new study has an important limitation of its own.

One major caveat is that the data set only includes family income as opposed to individual income. This opens up additional channels through which physical appearance could affect family income, Song explained. In this study, we identified the combined association between body shapes and family income through the labor market and marriage market. Thus, further investigations with a new survey on individual income would be an interesting direction for the future research.

The study, Body shape matters: Evidence from machine learning on body shape-income relationship, was published July 30, 2021.

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Deep machine learning study finds that body shape is associated with income - PsyPost

Apple began discussing integrating health sensors into future sports-oriented headphones in a patent application that was published back in April 2009 and filed in 2008. Apple's engineers noted at the time that "The sensor can also be other than (or in addition to) an activity sensor, such as a psychological or biometric sensors which could measure temperature, heartbeat, etc. of a user of the monitoring system." Fast forwarding to 2018, Apple decided to update their AirPods trademark by adding "wellness sensors" to its description, a telltale sign something was in-the-works. Then a series of patents surfaced in 2020-21 timeline covering health sensor for future AirPods (01,02&03). To top it all off, in June of this year, Apple's VP of Technology talked about health sensors on Apple Watch and possibly AirPods.

The latest development on this front came from Apple's Machine Learning (ML) Research team earlier this month in the form of a research paper. Apple notes, "In this paper, we take the first step towards developing a breathlessness measurement tool by estimating respiratory rate (RR) on exertion in a healthy population using audio from wearable headphones. Given this focus, such a capability also offers a cost-effective method to track cardiorespiratory fitness over time. While sensors such as thermistors, respiratory gauge transducers, and acoustic sensors provide the most accurate estimation of a persons breathing patterns, they are intrusive and may not be comfortable for everyday use. In contrast, wearable headphones are relatively economical, accessible, comfortable, and aesthetically acceptable."

Further into the paper, Apple clarifies: "All data was recorded using microphone-enabled, near-range headphones, specifically Apples AirPods. These particular wearables were selected because they are owned by millions and utilized in a wide array of contexts, from speaking on the phone to listening to music during exercise."

(Click on image to greatly Enlarge)

Below is a full copy of the research paper published by Apple's Machine Learning Research team in the form of a SCRBD document, courtesy of Patently Apple.

Machine Learning Team Paper on Respiratory Rates in Wearable Microphones by Jack Purcher on Scribd

While the paper doesn't discuss when these specialized sensors using machine learning techniques will be implemented in AirPods, it's clearly a positive development that Apple is well into the process of proving the value of adding such sensors to future AirPods.

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Apple's Machine Learning Research Team have Published a Paper on using Specialized Health Sensors in Future AirPods - Patently Apple