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What Happened?

So, let’s get the obvious out of the way: Something in iOS’ core operating system was leaked to GitHub by an unknown person, which led to a moderate amount of noise and a pretty large amount of panic.

Apple’s modus operandi usually involves trying to keep the code for its operating systems as locked down as possible, especially since it depends on a blend of hardware and software that could be reverse-engineered if anyone took a good enough look at it.

Unlike other components of iOS whose source code was released by Apple on occasion, the company took painstaking efforts to make sure that iBoot’s code never reached the wrong hands since it is a sort of “master key” that unlocks the ability to run iOS on other hardware in many instances.

Can Hackers Take Advantage of This?

Although iBoot’s code could be (and has been) reverse-engineered at any point in time, most hackers won’t be interested in some code that may or may not imitate Apple’s original stuff.

A good hacker could reverse-engineer something very similar to iBoot but could never reproduce the full product. For both counterfeiters and hackers, having an original copy is important.

At this point, there are certainly many people interested in poking through iBoot, looking for holes to exploit. Surely, both security researchers and hackers are hard at work on that as you’re reading this.

However, we must point out that the code that was leaked belongs to iOS 9, meaning that a good portion of it might be outdated. On the other hand, it could provide some valuable insight on how Apple’s pre-boot process works and allow counterfeiters to create their own platforms that run iOS, boosting the “iPhone copycat” market.

There’s also the fact that vulnerabilities found in iOS 9’s iBoot could still work perfectly fine on hardware running iOS 11. Although iPhone hardware changes frequently, things strictly related to bootup don’t often “need” to change along with it.

For those who are worried about a mass infection of Apple devices, it would take quite an effort to actually do damage by exploiting an iBoot vulnerability. The reason for this is that Apple has multiple layers of fail-safes in both its hardware and software that might make a full-blown infection difficult.

Miguel Leiva-Gomez

Miguel has been a business growth and technology expert for more than a decade and has written software for even longer. From his little castle in Romania, he presents cold and analytical perspectives to things that affect the tech world.

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What Predictive Analytics Means For Businesses Today

Businesses have more data from various sources at their disposal than they may think.

With predictive analytics, you can use past information to project future outcomes for your business.

Analytics help you identify future opportunities, serve customers better and make more informed business decisions over time.

This article is for business owners and managers who want to leverage data and predictive analytics to bring thoughtful change to their organization.

Applying predictive analytics to a business or organization requires specialized software. It’s offered by several vendors, including IBM, SAP and SAS. It crunches the collected data to determine the specific answers a business is looking for.

While each software offering has different capabilities and user interfaces, the premise is the same. They all work by first analyzing all the information a company collects. This includes sales and customer statistics, employee productivity, and social media data.

They then plug that data into predictive models. They can project future trends and problems based on that past behavior by using specially created algorithms.

Tip

Be sure to use relevant, accurate data with a considerable sample size. Otherwise, predictive analytics may not produce reliable insights.

These models can help businesses predict various consumer trends, as well as employee productivity shifts, to help drive supply and marketing decisions and improve efficiency.

While predictive analytics software used to be an option for larger organizations only, recent developments have made it more accessible to small businesses. This type of software, which is available from vendors such as Emanio and Angoss, is now sold at more affordable prices. It can be run from any personal computer instead of needing to be installed directly on a company’s server.

Examples of predictive analytics

Predictive analytics was originally used by large retailers and financial institutions. Today, businesses in every industry and of all sizes employ it to get a jump on the competition.

According to IBM, businesses can use predictive analytics in many different ways, such as these:

Uncovering hidden patterns and associations

Enhancing customer retention

Improving cross-selling opportunities through personalized offers and experiences

Maximizing productivity and profitability by aligning people, processes and assets

Reducing risk to minimize exposure and loss

Extending the useful life of equipment

Decreasing the number of equipment failures and maintenance costs

Focusing maintenance activities on high-value problems

Increasing customer satisfaction

For example, Sephora analyzes customers’ purchase histories and preferences to predict which products will most appeal to them. These tailored recommendations have led to 80% of its customers being completely loyal to the company. Similarly, Harley-Davidson uses predictive analytics to highlight potential high-value customers whom marketing agents and salespeople can target.

The popularity of predictive analytics with businesses has led to other types of organizations using the software. For example, healthcare firms use it to predict how certain drugs and therapies will be received by patients and help doctors better detect early warning signs for life-threatening diseases and illnesses.

Government bodies use predictive analytics software to help prevent crime, deliver social services and better serve residents. For example, more than two dozen U.S. cities use predictive analytics to determine where different crimes are most likely to occur. They then use this data to allocate resources appropriately, fighting crime while reducing costs.

Moving forward, businesses that don’t use predictive analytics software to drive their decisions will find themselves in the vast minority.

Pros and cons of predictive analytics

While predictive analytics holds vast potential, according to BDO Digital, just 19% of midsize companies are actively planning analytics initiatives. Part of that is because the technology comes with some potential downsides. Here’s a look at the benefits and drawbacks of predictive analytics today.

Pros

It provides actionable insights to help you get ahead of the competition.

It saves time that would otherwise be used for manual research and testing.

It can lower ongoing expenses through workflow optimization.

It may reduce wasted capital on ineffective marketing campaigns.

It becomes more reliable as time goes on.

Cons

It takes time to produce meaningful results.

It requires considerable data-gathering efforts and preparation upfront.

It may come with high upfront costs and initial disruptions.

Making the most of predictive analytics

Given the potential drawbacks, you need to apply predictive analytics correctly to experience its benefits. One of the most important considerations is to use reliable, clean data.

If these algorithms don’t have high-quality data, they won’t produce accurate results. Consequently, organizations believe that bad information is costing them $15 million a year in losses, according to research by Gartner. You can avoid this by collecting data from reliable sources and cleansing it before feeding it into predictive models. That includes verifying it against other sources, removing redundancies and standardizing its format.

As with any new technology, it’s also best to start small. You can minimize the initial expense and disruptions by applying predictive analytics to one area first, then slowly expanding it as your company learns to manage it. This will also help your employees understand how to work with these technologies more effectively.

Finally, you should regularly review your predictive analytics data to ensure it remains reliable. As situations change, algorithms will likely need tweaks and adjustments. Monitoring their performance can help your business experience the benefits without assuming too much risk.

Predictive analytics revolutionizing business

Predictive analytics has changed the way many businesses operate. Companies across virtually every industry have seen remarkable improvements after implementing this technology. It could become the norm as more people realize these benefits.

Like any technology, predictive analytics is not a cure-all. It won’t solve every problem a company faces, especially without careful planning and implementation, but it can offer substantial help. It will undoubtedly change the way business works.

Chad Brooks contributed to the writing and research in this article. 

Code Interpreter Available For Chatgpt Plus Users After Maintenance

OpenAI announced on Twitter that all ChatGPT Plus users will have access to Code Interpreter.

Code Interpreter will be available to all ChatGPT Plus users over the next week.

It lets ChatGPT run code, optionally with access to files you’ve uploaded. You can ask ChatGPT to analyze data, create charts, edit files, perform math, etc.

— OpenAI (@OpenAI) July 6, 2023

Code Interpreter is an official ChatGPT plugin for data analytics, image conversions, editing code, and more. It recently went from the Alpha to Beta experimental stage.

ChatGPT Plus users can enable access to Code Interpreter and other experimental new features in their account settings.

Code Interpreter For SEO

Digital marketing professionals have found unique ways to use Code Interpreter for search engine optimization (SEO).

Welcome to SEO 2.0:

Code Interpreter revolutionizes SEO by analyzing search engine algorithms & user behavior, generating data-driven insights for content optimization, & boosting site rankings with tailor-made strategies

Millions will be made as new companies hit new rankings

— GREG ISENBERG (@gregisenberg) May 4, 2023

Simply imagine it as a new way to analyze any exportable marketing data.

Feed your campaign and ad data into ChatGPT. 

Ask it to analyze the data and report on key performance metrics.

It will identify your weak spots, strengths, and areas for testing.

— Micheal O’Neill (@heymikeoneill) June 18, 2023

Using Code Interpreter with Google Search Console data seems to be a popular use so far.

— Greg Bernhardt 🐍🌊 (@GregBernhardt4) May 9, 2023

Code interpreter🤯

— Dr. Marie Haynes🌱 (@Marie_Haynes) July 7, 2023

It worked!

ChatGPT Code Interpreter talked me through all the steps in connecting with the GSC API.

— Dr. Marie Haynes🌱 (@Marie_Haynes) July 10, 2023

As the access to Code Interpreter increases, so should examples of new ways to use it and other ChatGPT plugins for marketing and SEO.

Playing with the Code Interpreter for ChatGPT w/ Local SEO Data.

I was looking at firm names in Atlanta and found the most common words in PI law firm names.

— Casey Meraz (@CaseyMeraz) July 10, 2023

Code Interpreter Disabled Temporarily For Maintenance

Update: July 14, 2023, 23:05 PM PDT

OpenAI performed routine maintenance on the Code Interpreter plugin, disabling it temporarily for users.

Just unavailable temporarily while the team fixes stuff.

This is why we do Beta’s!

— chúng tôi (@OfficialLoganK) July 14, 2023

Reports of the red lightning bolt symbol had been reported on Twitter and in the OpenAI community forum.

— Min Choi (@minchoi) July 14, 2023

Some users saw that the plugin was disabled.

— Gavriel Cohen (@Gavriel_Cohen) July 14, 2023

OpenAI’s status indicator eventually showed the feature was under maintenance and became operational again.

There were a few additional performance issues with ChatGPT over the past week, all of which were resolved quickly.

OpenAI Legal Troubles And Privacy Concerns

Code Interpreter’s Beta launch came a few days after OpenAI had to disable Browse with Bing, the official ChatGPT plugin for web browsing. OpenAI is still working on a fix to protect content creators and publishers from Browse with Bing, allowing users to bypass paywalls.

OpenAI also experienced a massive breach of user credentials and faced legal action over using copyrighted books and users’ personal data to train its models.

The ChatGPT app addresses privacy practices on the OpenAI website, in privacy disclaimers, and on the app’s opening screen.

Despite these concerns, the App Store places ChatGPT as #1 in the productivity category, averaging 4.4 stars from over 10.1k users.

While the app is doing well, SimilarWeb reports show traffic has slightly declined to the ChatGPT website over the past 28 days.

New Feature Releases Amidst Controversy

OpenAI’s announcement of the Code Interpreter as an official plugin for ChatGPT Plus users marks a significant step in enhancing the capabilities of the AI language model.

However, this launch comes at a time when OpenAI is grappling with privacy concerns and legal issues.

OpenAI must address and alleviate the concerns surrounding data protection and copyright issues, especially considering the sensitive nature of executing code and uploading files using Code Interpreter or other ChatGPT plugins.

Otherwise, users concerned with privacy and generative AI content quality will turn to ChatGPT alternatives that outperform the leading AI chatbots.

Featured image: Shutter.Ness/Shutterstock

What The ‘Hottest Day On Record’ Really Means

It seems like every couple of days now there is a new record being broken—just today Northern Ireland hit its highest ever temperature recorded as other parts of Europe deal with flooding and rain in rare quantities. 

Some records are a little trickier to define than others, though. Take Death Valley in California as an example. This is the hottest, driest, and lowest national park in the US and is no stranger to intense heat. High temperature averages are 100 degrees Fahrenheit and above for at least a third of the year, and just last week produced the highest daily average temperature observed on the planet at 118.1 degrees. On Friday, July 9, the high temperature reached a scorching 130 degrees Fahrenheit, one of the highest recorded temperatures of all time.

But every time Death Valley has a wildly high temperature, a weird historical artifact pops up—the one time in 1913 that the temperature was recorded as 134 degrees Fahrenheit. Over a century ago, US Weather Bureau observer Oscar Denton measured a week-long string of suspiciously high temperatures in Death Valley’s Greenland Ranch. If you look at the other temperatures recorded nearby, this recording stood out like a sore thumb—temperatures creeping into the high 120’s didn’t start reappearing until the late 1990s when the IPCC had begun to recognize global warming. 

While that reading is still technically listed as the world’s hottest temperature ever recorded, some argue that this month’s high is likely a more realistic record. 

“If Friday’s observation passes an investigation (instrument calibration, etc.) then, yes, this is a new reliably measured global extreme heat record,” weather records expert Christopher Burt told Yale Climate Connections. That’s because it was shockingly tricky to get accurate temperature readings in the past, which makes it difficult to know which temperatures are legitimate and which aren’t.

How we record temperatures

Like many modern-day data capturing methods, temperatures (as well as precipitation, cloud cover, wind speed and pressure) have been collected automatically in Automated Surface Observing Systems since the mid-90s, explains Jon Erdman, a senior meteorologist at the Weather Channel. There are over 900 of these systems throughout the US, and on average report weather data around every 20 minutes. 

But the thermometer that records temperatures all day every day from stations around the world isn’t exactly like the ones you might have outside your house, Erdman adds. Official weather-taking thermometers need to be shielded from the sun and wind, and have what looks like little lampshades on them so that they can measure the actual surface temperature without being influenced by other sources. 

[Related: Heat is the silent killer we should all be worried about.]

“If you don’t shield the thermometer from direct sunlight, it’s going to heat up more,” he says. So if you’re ever wondering why your personal thermometer might show a bit more heat than the Weather Channel is showing, that could be a reason why. 

Scientists have been shielding their thermometers since the late 1800s, starting with the Stevenson screen, which looked a bit like a little white birdhouse or cabinet and held a variety of weather-investigating tools. Like today’s setup, this contraption protected the thermometer from the elements to just get surface temperature.

In the 21st century, the setup hasn’t changed much—the key difference is just how involved humans used to be. “What you’ll see in these [historical] measurements is literally hand-written measures of every single day, going and seeing what that thermometer says,” says Karen McKinnon, a professor of statistics and sustainability at UCLA.

So, occasional errors are completely within expectation. But how could a person record such a high temperature by accident? From a technological standpoint, in a place as toasty as Death Valley, even a little bit of direct sunlight or scorching hot sand brushing against the meter could rack up some extra degrees, Erdman says. 

McKinnon has also found in her research that people often round up to preferentially chosen numbers rather than be uncertain about the exact temperature. Then, when it comes to the Global Historical Climate Network Daily database, everything gets rounded up to the closest 0.1 degree Celsius, despite the level of precision it was initially recorded. In her findings, 63 percent of temperature samples were biased by this double-rounding. 

[Related: Are ‘heat days’ the new snow days?]

In a lengthy 2024 take-down of the record, geographer William T. Reid and weather historian Christopher Burt also raised concerns about the observers’ credibility to accurately record temperatures, exaggerating the temperature beyond what anyone could explain meteorologically. 

“Things can always go wrong,” McKinnon says. “People can misrecord temperatures, which is probably the most likely situation”

Why does any of this matter?

This isn’t the first time a global heat record has been challenged. Back in 2012, a reading of 136.4 degrees Fahrenheit in El Azizia, Libya on Sept. 13, 1922, was removed from its record-holding spot after an international team found five major concerns with the recording, from unreliable equipment to an inexperienced observer. And there’s certainly a chance that the 1913 Death Valley record could come into reassessment under the National Climate Extremes Committee. 

And even though our systems are much more accurate nowadays, with automated readings and no deciphering of hand-written notes, there’s still a chance that our modern systems may mess up. “You can certainly have problems at individual sites,” Erdman says, “But those are pretty rare.” Just take some of Tampa’s 2023 record highs that ended up being inaccurate due to being too closely located to toasty parking lots and highways, picking up too-high temperatures. 

Just like every anomaly, there’s a chance that the old record was truly a one-in-a-million occurrence. But whether it stands or not, what really matters is that these records are being broken more and more often in a pattern, we cannot ignore—temperatures are rising worldwide, and the entire planet is now experiencing the wrath of intensified weather. 

“What you’re gonna see is that there’s an upward trend,” McKinnon says. “And you can see breaking records with a greater probability.”  

Nights Are Warming Faster Than Days. Here’s What That Means For The Planet.

Climate change can have profound impacts across ecosystems, but rising average temperatures are just one factor among many driving those repercussions. A new study published in late September in Global Change Biology found that nighttime temperatures are increasing at a faster rate compared to daytime temps in most land areas across the Earth. That shift can influence everything from predator-prey dynamics to plant growth.

“Climate change is already messing things up,” says Daniel Cox, an ecologist at the University of Exeter and lead author of the study. “But the 24-hour asymmetry is adding an extra dimension of complexity [for species].”

Previous analyses have found that the rising greenhouse gases in our atmosphere are not having an even effect on temperatures from day to night. But Cox says this is the first study of temperature asymmetries to cover all global lands.

Without understanding these effects, ecologists can’t hope to fully grasp how the natural world will respond. Experiments with grasshoppers and spiders, for example, have shown that the time of the day at which heating occurs can tip the ecological balance. In a 2023 study, researchers found increased daytime warming led spiders to seek cover earlier in the day, enabling grasshoppers to munch away at plants without fear, affecting plant growth. Conversely, the spiders hunted the grasshoppers more fiercely when nightime temperatures warmed, possibly reducing the insect’s numbers. These kinds of effects can ripple across a larger ecosystem, with potential impacts for plant communities, wildlife, and agriculture.

Nighttime ecology is particularly understudied, with most research focused on the daytime activities of organisms. That’s why Cox was interested in understanding temperature asymmetries across the globe. Revealing these differences can be a stepping stone toward seeing how nocturnal activities are faring under climate change.

To find out, Cox and his team mapped 35 years of data on temperature, cloud cover, humidity, and precipitation. For each of the pixels of land area on the global maps, they looked at how the maximum daytime and minimum nighttime temperatures changed over time.

On a global level, nights are heating up more than days. Almost twice as much area has seen a greater temperature increase at night compared to during the day. This change appears to be closely tied to shifts in cloud cover; where cloud cover increased, so did nighttime temperatures. “We think that cloud cover is probably the main driver [of increased nighttime temperatures],” says Cox.

In wetter regions, warming temperatures are causing more water to evaporate, leading to more clouds. Clouds cool during the day by blocking the sun, but at night they trap heat close to the ground. In general, wetter regions over time got cloudier and thus warmer at night. Meanwhile, a smaller portion of already-arid land grew drier as temperatures increased the most during the day.

Cox also included leaf area index in the study, which is a measure plant canopy coverage used to estimate the productivity of flora. Overall, temperature jumps in either extreme—extra hot days or nights—harmed leaf area. With greater nighttime warming, plant respiration (using energy from photosynthesis to grow) at night increases, but the cloudier days make it harder to obtain this energy as photosynthesis requires sunlight. “The paper is good, timely, and important,” says Brandon Barton, an ecologist at Mississippi State University who wasn’t involved in the research. “Very few studies have done a good job at having multiple factors [associated with climate change].” Climate change isn’t simply evenly warming things up; temperatures are changing at different rates between seasons or times of day. Wind, rainfall, and snow patterns are also changing.

For Cox, the research will enable him to continue studying how animals are coping with climate change at night. “We know the world is changing through human influences, but we don’t really have a grasp of how the nighttime is changing,” he says. “It’s a big hole in the literature that has just been missed.”

Making The Grade: Apple Configurator 1.0 Set The Groundwork For Modern Ios Management

When the iPad was released in 2010, we were already using iPod touches in the classroom along with the old MacBooks. The entire management process around Apple’s devices was less straightforward. With the iPod touches, we would buy Apple directly from the iTunes store, and then sync them using a case similar to this one. It was slow, cumbersome, and prone to error as I had to often force quit iTunes and restart the process.

Everything changed in 2012 when Apple released Apple Configurator 1.0.

Apple Configurator for OS X Lion makes it easy for anyone to deploy iPhone, iPad and iPod touch in their school or business. Apple Configurator can be used to quickly configure large numbers of iOS devices with the settings, apps and data you specify for your students, employees or customers. You can prepare a set of new iOS devices that are configured only once and then deployed to users. Update devices to the latest version of iOS, install configuration profiles and apps, and enroll them with your organization’s Mobile Device Management solution, then hand them out. Preparing devices is a great deployment option for enterprises and schools where provide iOS devices to employees or students for their day-to-day professional or educational use. You can supervise a set of iOS devices that you want to control and configure on an ongoing basis. Apply a configuration to each device, and then reapply it after each use simply by reconnecting the device back to Apple Configurator. Supervision is an ideal option for loaning iOS devices to customers (for example, hotels, restaurants, and hospitals), sharing devices among students in a classroom or a lab, or deploying devices for dedicated tasks (for example, retail, field service, or medical). You can assign supervised devices to specific users in your organization. Check out a device to a user and restore the user’s backup (including all their data) to that device; then check the device back in and back up the user’s data for later use, possibly on a completely different device. This works well in educational settings where students need to be able to work with the same data and documents over a prolonged period of time, regardless of which device they are given.

As you can see, a lot of the terminology still used by Apple today dates back to the introduction of Apple Configurator 1.0. This app was released with support for Apple’s newly announced Volume Purchase Program to support licensing apps in bulk. The process wasn’t as streamlined as it is now, though.

After getting the apps installed, you could also configure additional options like Wi-Fi password and customize iOS restrictions. Knowing what we have now, it seems like an elementary product, but at the time, it was light years ahead of anything that was accessible to schools. Apple was still building out its MDM APIs, and this app was a free way to manage devices. It brought some new concepts (Supervision) to iPad management that we still use today.

Apple Configurator is now on version 2, and its roughly still the same core app that it has always been. The workflows are a lot different. Personally, I only use it to kickstart some of my deployment stuff, but otherwise, I use Jamf for everything iPad management wise.

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