Find some of our favorite resources for the April 8, 2024, solar eclipse, including apps, video explainers, children's activities, and books.

The post Eclipse Apps, Books, Videos: Resources for the 2024 Total Solar Eclipse appeared first on Sky & Telescope.

In this episode, Tyler demonstrates how to create and use Safari website shortcuts or "Web apps" on macOS.

This feature allows you to create shortcuts to websites on your dock that when opened, behave as if the website was its own app. This may be useful for VoiceOver users, as web apps are included when pressing Command-Tab to cycle through open apps, making them easy to switch to and from. In addition, these web apps can be mapped to commands in Keyboard Commander so they can be accessed with a single keystroke.

To create a web app, go to a website in Safari and choose File > Add to Dock. Change the name and icon of the web app if you wish, and click Add. It can then be accessed via the Dock, or in the Applications folder in your Home folder, accessed by choosing Go > Home in Finder, or pressing Command-Shift-H. To map the web app to a command in Keyboard Commander, open Voiceover Utility > Commanders > Keyboard, click Add, type the key that you want to open the app when pressed, choose "Custom commands > Open application" from the menu, and navigate to and select the web app in the Open dialog.

Enhance the experience for your audience by making unique artwork for each episode. But you need to adapt to how each app might display the image.

The post How to Make Episode Artwork Show in Podcast Apps (even in Apple Podcasts!) first appeared on The Audacity to Podcast.

Whether you're just starting your podcast or you've been podcasting for a while, you might find some things easier by using better tools. Here are the audio-editing apps I recommend most for podcasting in 2023.

The post Best Audio-Editing Apps for Podcasting in 2023 first appeared on The Audacity to Podcast.

If your podcast is listed twice in Apple Podcasts or another podcast directory, STOP! Don't do anything about it until you read or listen to this!

The post How to Fix Duplicate Listings in Apple Podcasts (or Other Podcast Apps) first appeared on The Audacity to Podcast.

I've tried a bunch of pomodoro-style apps that I haven't done well with. I recently found one that's like Pomodoro except there's a growing plant, and if you mess with your phone you'll kill the plant. This works for me for whatever reason. I was wondering if anyone knew of other apps like that; especially ones with more real-world consequences. I guess my will isn't strong enough to not mess with my phone just because some timer is counting down. But the charming messages about the poor little plant and the cute drawings appeal to me apparently.

I saw this app PocketPoints, which would be great except it only works at participating schools.

I'm also not interested in those apps that quantify how often you check your phone.

This is for iOS please.

Thanks!

Quinn Nelson, renowned host of [Snazzy Labs](https://www.youtube.com/@snazzy), returns to the show to recap the highlights of WWDC: Apple Intelligence, platform updates, and the latest salvos from the EC regarding Apple’s compliance with the DMA.

En 6AM Hoy por Hoy de Caracol Radio estuvo José Daniel López, Director ejecutivo de “Alianza in”, para hablar sobre el halón de orejas de la Superintendencia de Transporte por la falta de sanción al transporte informal en Colombia.

BlackHole is a modern macOS virtual audio driver that allows applications to pass audio to other applications with zero additional latency. Perfect for Streamers, Podcasters, and Online Instructors.

He sat motionless as the judge gave him his sentence.

Cybersecurity researchers are warning that a command-and-control (C&C) framework called Winos is being distributed within gaming-related applications like installation tools, speed boosters, and optimization utilities. "Winos 4.0 is an advanced malicious framework that offers comprehensive functionality, a stable architecture, and efficient control over numerous online endpoints to execute

Cybersecurity researchers have disclosed new security flaws impacting Citrix Virtual Apps and Desktop that could be exploited to achieve unauthenticated remote code execution (RCE) The issue, per findings from watchTowr, is rooted in the Session Recording component that allows system administrators to capture user activity, and record keyboard and mouse input, along with a video stream of the

Lately, we’ve noticed an increased number of reports indicating issues with Blogger posting campaigns, where the error message “(403) We’re sorry, but one or more limits for the requested action have been exceeded” is displayed. To make it easier for you, in Blogger Poster we use our own Google Cloud app to make posts to […]

The post Blogger Poster custom apps for volume posting appeared first on RSSground.com.

Microsoft is planning to no longer support the Windows Mail, Calendar, and People apps later this year. The Verge: The software giant has been moving existing users of these apps over to the new Outlook for Windows app in recent months, and now it has set an end of support date for the Mail, Calendar, and People apps of December 31st. Once the apps reach end of support later this year, Microsoft warns that users who haven't moved to the new Outlook app "will no longer be able to send and receive email using Windows Mail and Calendar." Microsoft has been rolling out the new Outlook for Windows app for years, with it officially reaching the general availability stage in August. The new web-based Outlook is designed to eventually replace the full desktop version of Outlook too, and Microsoft plans to provide enterprise customers a 12-month notice before it starts to move people away from the desktop version of Outlook.

Read more of this story at Slashdot.

Artificial Intelligence Apps Risk Entrenching India’s Socio-economic Inequities Expert comment sysadmin 14 March 2018

Artificial intelligence applications will not be a panacea for addressing India’s grand challenges. Data bias and unequal access to technology gains will entrench existing socio-economic fissures.

Artificial intelligence (AI) is high on the Indian government’s agenda. Some days ago, Prime Minister Narendra Modi inaugurated the Wadhwani Institute for Artificial Intelligence, reportedly India’s first research institute focused on AI solutions for social good. In the same week, Niti Aayog CEO Amitabh Kant argued that AI could potentially add $957 billion to the economy and outlined ways in which AI could be a ‘game changer’.

During his budget speech, Finance Minister Arun Jaitley announced that Niti Aayog would spearhead a national programme on AI; with the near doubling of the Digital India budget, the IT ministry also announced the setting up of four committees for AI-related research. An industrial policy for AI is also in the pipeline, expected to provide incentives to businesses for creating a globally competitive Indian AI industry.

Narratives on the emerging digital economy often suffer from technological determinism — assuming that the march of technological transformation has an inner logic, independent of social choice and capable of automatically delivering positive social change. However, technological trajectories can and must be steered by social choice and aligned with societal objectives. Modi’s address hit all the right notes, as he argued that the ‘road ahead for AI depends on and will be driven by human intentions’. Emphasising the need to direct AI technologies towards solutions for the poor, he called upon students and teachers to identify ‘the grand challenges facing India’ – to ‘Make AI in India and for India’.

To do so, will undoubtedly require substantial investments in R&D, digital infrastructure and education and re-skilling. But, two other critical issues must be simultaneously addressed: data bias and access to technology gains.

While computers have been mimicking human intelligence for some decades now, a massive increase in computational power and the quantity of available data are enabling a process of ‘machine learning.’ Instead of coding software with specific instructions to accomplish a set task, machine learning involves training an algorithm on large quantities of data to enable it to self-learn; refining and improving its results through multiple iterations of the same task. The quality of data sets used to train machines is thus a critical concern in building AI applications.

Much recent research shows that applications based on machine learning reflect existing social biases and prejudice. Such bias can occur if the data set the algorithm is trained on is unrepresentative of the reality it seeks to represent. If for example, a system is trained on photos of people that are predominantly white, it will have a harder time recognizing non-white people. This is what led a recent Google application to tag black people as gorillas.

Alternatively, bias can also occur if the data set itself reflects existing discriminatory or exclusionary practices. A recent study by ProPublica found for example that software that was being used to assess the risk of recidivism in criminals in the United States was twice as likely to mistakenly flag black defendants as being at higher risk of committing future crimes.

The impact of such data bias can be seriously damaging in India, particularly at a time of growing social fragmentation. It can contribute to the entrenchment of social bias and discriminatory practices, while rendering both invisible and pervasive the processes through which discrimination occurs. Women are 34 per cent less likely to own a mobile phone than men – manifested in only 14 per cent of women in rural India owning a mobile phone, while only 30 per cent of India’s internet users are women.

Women’s participation in the labour force, currently at around 27 per cent, is also declining, and is one of the lowest in South Asia. Data sets used for machine learning are thus likely to have a marked gender bias. The same observations are likely to hold true for other marginalized groups as well.

Accorded to a 2014 report, Muslims, Dalits and tribals make up 53 per cent of all prisoners in India; National Crime Records Bureau data from 2016 shows in some states, the percentage of Muslims in the incarcerated population was almost three times the percentage of Muslims in the overall population. If AI applications for law and order are built on this data, it is not unlikely that it will be prejudiced against these groups.

(It is worth pointing out that the recently set-up national AI task force is comprised of mostly Hindu men – only two women are on the task force, and no Muslims or Christians. A recent article in the New York Times talked about AI’s ‘white guy problem’; will India suffer from a ‘Hindu male bias’?)

Yet, improving the quality, or diversity, of data sets may not be able to solve the problem. The processes of machine learning and reasoning involve a quagmire of mathematical functions, variables and permutations, the logic of which are not readily traceable or predictable. The dazzle of AI-enabled efficiency gains must not blind us to the fact that while AI systems are being integrated into key socio-economic systems, their accuracy and logic of reasoning have not been fully understood or studied.

The other big challenge stems from the distribution of AI-led technology gains. Even if estimates of AI contribution to GDP are correct, the adoption of these technologies is likely to be in niches within the organized sector. These industries are likely to be capital- rather than labour-intensive, and thus unlikely to contribute to large-scale job creation.

At the same time, AI applications can most readily replace low- to medium-skilled jobs within the organized sector. This is already being witnessed in the outsourcing sector – where basic call and chat tasks are now automated. Re-skilling will be important, but it is unlikely that those who lose their jobs will also be those who are being re-skilled – the long arch of technological change and societal adaptation is longer than that of people’s lives. The contractualization of work, already on the rise, is likely to further increase as large industries prefer to have a flexible workforce to adapt to technological change. A shift from formal employment to contractual work can imply a loss of access to formal social protection mechanisms, increasing the precariousness of work for workers.

The adoption of AI technologies is also unlikely in the short- to medium-term in the unorganized sector, which engages more than 80 per cent of India’s labor force. The cost of developing and deploying AI applications, particularly in relation to the cost of labour, will inhibit adoption. Moreover, most enterprises within the unorganized sector still have limited access to basic, older technologies – two-thirds of the workforce are employed in enterprises without electricity. Eco-system upgrades will be important but incremental. Given the high costs of developing AI-based applications, most start-ups are unlikely to be working towards creating bottom-of-the-pyramid solutions.

Access to AI-led technology gains is thus likely to be heavily differentiated – a few high-growth industries can be expected, but these will not necessarily result in the welfare of labour. Studies show that labour share of national income, especially routine labour, has been declining steadily across developing countries.

We should be clear that new technological applications themselves are not going to transform or disrupt this trend – rather, without adequate policy steering, these trends will be exacerbated.

Policy debates about AI applications in India need to take these two issues seriously. AI applications will not be a panacea for addressing ‘India’s grand challenges’. Data bias and unequal access to technology gains will entrench existing socio-economic fissures, even making them technologically binding.

In addition to developing AI applications and creating a skilled workforce, the government needs to prioritize research that examines the complex social, ethical and governance challenges associated with the spread of AI-driven technologies. Blind technological optimism might entrench rather than alleviate the grand Indian challenge of inequity and growth.

This article was originally published in the Indian Express.

Teachers are using a variety of games, videos, and apps to supplement online math instruction—but not all of them are created equal.

Zookeepers at the National Zoo keep orangutans mentally stimulated with an innovative use of iPads

North Korean hackers have disguised malware in seemingly harmless macOS apps using sophisticated code to bypass security checks and target unsuspecting users.

Bitget Wallet and Foresight Ventures have collaborated to invest $20 million (roughly Rs. 168 crore) in Telegram, aiming to accelerate the growth of its Mini Apps initiative, as announced on Monday, November 11.

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The Roku Ultra LT streaming device is $34 at Walmart, down from the list price of $79.99. That's a 57% discount.

Both hit weeks after OpenAI released a ChatGPT app.

Nearly every day since she was a child, Alex Leow, a psychiatrist and computer scientist at the University of Illinois Chicago, has played the piano. Some days she plays well, and other days her tempo lags and her fingers hit the wrong keys. Over the years, she noticed a pattern: How well she plays depends on her mood. A bad mood or lack of sleep almost always leads to sluggish, mistake-prone music.

In 2015, Leow realized that a similar pattern might be true for typing. She wondered if she could help people with psychiatric conditions track their moods by collecting data about their typing style from their phones. She decided to turn her idea into an app.

After conducting a pilot study, in 2018 Leow launched BiAffect, a research app that aims to understand mood-related symptoms of bipolar disorder through keyboard dynamics and sensor data from users’ smartphones. Now in use by more than 2,700 people who have volunteered their data to the project, the app tracks typing speed and accuracy by swapping the phone’s onscreen keyboard with its own nearly identical one.

The software then generates feedback for users, such as a graph displaying hourly keyboard activity. Researchers get access to the donated data from users’ phones, which they use to develop and test machine learning algorithms that interpret data for clinical use. One of the things Leow’s team has observed: When people are manic—a state of being overly excited that accompanies bipolar disorder—they type “ferociously fast,” says Leow.

Compared to a healthy user [top], a person experiencing symptoms of bipolar disorder [middle] or depression [bottom] may use their phone more than usual and late at night. BiAffect measures phone usage and orientation to help track those symptoms. BiAffect

BiAffect is one of the few mental-health apps that take a passive approach to collecting data from a phone to make inferences about users’ mental states. (Leow suspects that fewer than a dozen are currently available to consumers.) These apps run in the background on smartphones, collecting different sets of data not only on typing but also on the user’s movements, screen time, call and text frequency, and GPS location to monitor social activity and sleep patterns. If an app detects an abrupt change in behavior, indicating a potentially hazardous shift in mental state, it could be set up to alert the user, a caretaker, or a physician.

Such apps can’t legally claim to treat or diagnose disease, at least in the United States. Nevertheless, many researchers and people with mental illness have been using them as tools to track signs of depression, schizophrenia, anxiety, and bipolar disorder. “There’s tremendous, immediate clinical value in helping people feel better today by integrating these signals into mental-health care,” says John Torous, director of digital psychiatry at Beth Israel Deaconess Medical Center, in Boston. Globally, one in 8 people live with a mental illness, including 40 million with bipolar disorder.

These apps differ from most of the more than 10,000 mental-health and mood apps available, which typically ask users to actively log how they’re feeling, help users connect to providers, or encourage mindfulness. The popular apps Daylio and Moodnotes, for example, require journaling or rating symptoms. This approach requires more of the user’s time and may make these apps less appealing for long-term use. A 2019 study found that among 22 mood-tracking apps, the median user-retention rate was just 6.1 percent at 30 days of use.

App developers are trying to avoid the pitfalls of previous smartphone-psychiatry startups, some of which oversold their capabilities before validating their technologies.

But despite years of research on passive mental-health apps, their success is far from guaranteed. App developers are trying to avoid the pitfalls of previous smartphone psychiatry startups, some of which oversold their capabilities before validating their technologies. For example, Mindstrong was an early startup with an app that tracked taps, swipes, and keystrokes to identify digital biomarkers of cognitive function. The company raised US $160 million in funding from investors, including $100 million in 2020 alone, and went bankrupt in February 2023.

Mindstrong may have folded because the company was operating on a different timeline from the research, according to an analysis by the health-care news website Stat. The slow, methodical pace of science did not match the startup’s need to return profits to its investors quickly, the report found. Mindstrong also struggled to figure out the marketplace and find enough customers willing to pay for the service. “We were first out of the blocks trying to figure this out,” says Thomas Insel, a psychiatrist who cofounded Mindstrong.

Now that the field has completed a “hype cycle,” Torous says, app developers are focused on conducting the research needed to prove their apps can actually help people. “We’re beginning to put the burden of proof more on those developers and startups, as well as academic teams,” he says. Passive mental-health apps need to prove they can reliably parse the data they’re collecting, while also addressing serious privacy concerns.

Passive sensing catches mood swings early

A crucial component of managing psychiatric illness is tracking changes in mental states that can lead to more severe episodes of the disease. Bipolar disorder, for example, causes intense swings in mood, from extreme highs during periods of mania to extreme lows during periods of depression. Between 30 and 50 percent of people with bipolar disorder will attempt suicide at least once in their lives. Catching early signs of a mood swing can enable people to take countermeasures or seek help before things get bad.

But detecting those changes early is hard, especially for people with mental illness. Observations by other people, such as family members, can be subjective, and doctor and counselor sessions are too infrequent.

That’s where apps come in. Algorithms can be trained to spot subtle deviations from a person’s normal routine that might indicate a change in mood—an objective measure based on data, like a diabetic tracking blood sugar. “The ability to think objectively about my own thinking is really key,” says retired U.S. major general Gregg Martin, who has bipolar disorder and is an advisor for BiAffect.

The data from passive sensing apps could also be useful to doctors who want to see objective data on their patients in between office visits, or for people transitioning from inpatient to outpatient settings. These apps are “providing a service that doesn’t exist,” says Colin Depp, a clinical psychologist and professor at the University of California, San Diego. Providers can’t observe their patients around the clock, he says, but smartphone data can help close the gap.

Depp and his team have developed an app that uses GPS data and microphone-based sensing to determine the frequency of conversations and make inferences about a person’s social interactions and isolation. The app also tracks “location entropy,” a metric of how much a user moves around outside of routine locations. When someone is depressed and mostly stays home, location entropy decreases.

Depp’s team initially developed the app, called CBT2go, as a way to test the effectiveness of cognitive behavioral therapy in between therapy sessions. The app can now intervene in real time with people experiencing depressive or psychotic symptoms. This feature helps people identify when they feel lonely or agitated so they can apply coping skills they’ve learned in therapy. “When people walk out of the therapist’s office or log off, then they kind of forget all that,” Depp says.

Another passive mental-health-app developer, Ellipsis Health in San Francisco, uses software that takes voice samples collected during telehealth calls to gauge a person’s level of depression, anxiety, and stress symptoms. For each set of symptoms, deep-learning models analyze the person’s words, rhythms, and inflections to generate a score. The scores indicate the severity of the person’s mental distress, and are based on the same scales used in standard clinical evaluations, says Michael Aratow, cofounder and chief medical officer at Ellipsis.

Aratow says the software works for people of all demographics, without needing to first capture baseline measures of an individual’s voice and speech patterns. “We’ve trained the models in the most difficult use cases,” he says. The company offers its platform, including an app for collecting the voice data, through health-care providers, health systems, and employers; it’s not directly available to consumers.

In the case of BiAffect, the app can be downloaded for free by the public. Leow and her team are using the app as a research tool in clinical trials sponsored by the U.S. National Institutes of Health. These studies aim to validate whether the app can reliably monitor mood disorders, and determine whether it could also track suicide risk in menstruating women and cognition in people with multiple sclerosis.

BiAffect’s software tracks behaviors like hitting the backspace key frequently, which suggests more errors, and an increase in typing “@” symbols and hashtags, which suggest more social media use. The app combines this typing data with information from the phone’s accelerometer to determine how the user is oriented and moving—for example, whether the user is likely lying down in bed—which yields more clues about mood.

Ellipsis Health analyzes audio captured during telehealth visits to assign scores for depression, anxiety, and stress.Ellipsis Health

The makers of BiAffect and Ellipsis Health don’t claim their apps can treat or diagnose disease. If app developers want to make those claims and sell their product in the United States, they would first have to get regulatory approval from the U.S. Food and Drug Administration. Getting that approval requires rigorous and large-scale clinical trials that most app makers don’t have the resources to conduct.

Digital-health software depends on quality clinical data

The sensing techniques upon which passive apps rely—measuring typing dynamics, movement, voice acoustics, and the like—are well established. But the algorithms used to analyze the data collected by the sensors are still being honed and validated. That process will require considerably more high-quality research among real patient populations.

Greg Mably

For example, clinical studies that include control or placebo groups are crucial and have been lacking in the past. Without control groups, companies can say their technology is effective “compared to nothing,” says Torous at Beth Israel.

Torous and his team aim to build software that is backed by this kind of quality evidence. With participants’ consent, their app, called mindLAMP, passively collects data from their screen time and their phone’s GPS and accelerometer for research use. It’s also customizable for different diseases, including schizophrenia and bipolar disorder. “It’s a great starting point. But to bring it into the medical context, there’s a lot of important steps that we’re now in the middle of,” says Torous. Those steps include conducting clinical trials with control groups and testing the technology in different patient populations, he says.

How the data is collected can make a big difference in the quality of the research. For example, the rate of sampling—how often a data point is collected—matters and must be calibrated for the behavior being studied. What’s more, data pulled from real-world environments tends to be “dirty,” with inaccuracies collected by faulty sensors or inconsistencies in how phone sensors initially process data. It takes more work to make sense of this data, says Casey Bennett, an assistant professor and chair of health informatics at DePaul University, in Chicago, who uses BiAffect data in his research.

One approach to addressing errors is to integrate multiple sources of data to fill in the gaps—like combining accelerometer and typing data. In another approach, the BiAffect team is working to correlate real-world information with cleaner lab data collected in a controlled environment where researchers can more easily tell when errors are introduced.

Who participates in the studies matters too. If participants are limited to a particular geographic area or demographic, it’s unclear whether the results can be applied to the broader population. For example, a night-shift worker will have different activity patterns from those with nine-to-five jobs, and a city dweller may have a different lifestyle from residents of rural areas.

After the research is done, app developers must figure out a way to integrate their products into real-world medical contexts. One looming question is when and how to intervene when a change in mood is detected. These apps should always be used in concert with a professional and not as a replacement for one, says Torous. Otherwise, the app’s assessments could be dangerous and distressing to users, he says.

When mood tracking feels like surveillance

No matter how well these passive mood-tracking apps work, gaining trust from potential users may be the biggest stumbling block. Mood tracking could easily feel like surveillance. That’s particularly true for people with bipolar or psychotic disorders, where paranoia is part of the illness.

Keris Myrick, a mental-health advocate, says she finds passive mental-health apps “both cool and creepy.” Myrick, who is vice president of partnerships and innovation at the mental-health-advocacy organization Inseparable, has used a range of apps to support her mental health as a person with schizophrenia. But when she tested one passive sensing app, she opted to use a dummy phone. “I didn’t feel safe with an app company having access to all of that information on my personal phone,” Myrick says. While she was curious to see if her subjective experience matched the app’s objective measurements, the creepiness factor prevented her from using the app enough to find out.

Keris Myrick, a mental-health advocate, says she finds passive mental-health apps “both cool and creepy.”

Beyond users’ perception, maintaining true digital privacy is crucial. “Digital footprints are pretty sticky these days,” says Katie Shilton, an associate professor at the University of Maryland focused on social-data science. It’s important to be transparent about who has access to personal information and what they can do with it, she says.

“Once a diagnosis is established, once you are labeled as something, that can affect algorithms in other places in your life,” Shilton says. She cites the misuse of personal data in the Cambridge Analytica scandal, in which the consulting firm collected information from Facebook to target political advertising. Without strong privacy policies, companies producing mental-health apps could similarly sell user data—and they may be particularly motivated to do so if an app is free to use.

Conversations about regulating mental-health apps have been ongoing for over a decade, but a Wild West–style lack of regulation persists in the United States, says Bennett of DePaul University. For example, there aren’t yet protections in place to keep insurance companies or employers from penalizing users based on data collected. “If there aren’t legal protections, somebody is going to take this technology and use it for nefarious purposes,” he says.

Some of these concerns may be mediated by confining all the analysis to a user’s phone, rather than collecting data in a central repository. But decisions about privacy policies and data structures are still up to individual app developers.

Leow and the BiAffect team are currently working on a new internal version of their app that incorporates natural-language processing and generative AI extensions to analyze users’ speech. The team is considering commercializing this new version in the future, but only following extensive work with industry partners to ensure strict privacy safeguards are in place. “I really see this as something that people could eventually use,” Leow says. But she acknowledges that researchers’ goals don’t always align with the desires of the people who might use these tools. “It is so important to think about what the users actually want.”

This article appears in the July 2024 print issue as “The Shrink in Your Pocket.”

Rogue Access Video - US

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The Russian App Store has lost 6,982 mobile apps since the start of the Ukraine invasion, as numerous companies have now pulled their apps and games from Apple’s iPhone and iPad App Stores in the country, according to data shared with TechCrunch by app intelligence firm Sensor Tower. To date, those apps had been downloaded […]

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When GitHub Copilot launched and started autocompleting lines of code — and, later, entire code snippets — the question many people were asking was: How long until we can just describe an app in natural language and Copilot will build it for us? We’ve seen quite a few experiments in this arena in recent months, […]

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Manchester : Manchester University Press, [2018]