AT&amp;T’s network went down for many of its customers across the United States Thursday morning, leaving customers unable to place calls, text or access the internet. By late morning, the company said most of its network had been restored.

AT&T says service has been restored after massive, nationwide outage. Authorities are investigating | CNN Business

Verizon benefits from a macroeconomic tailwind from the potential federal funds rate cut in 2024. Find out if VZ stock is a buy.

Verizon: Improving Profits, Macroeconomic Tailwind Also Expected (NYSE:VZ) | Seeking Alpha

AT&amp;T said the hourslong outage to its U.S. cellphone network Thursday appeared to be the result of a technical error, not a malicious attack.

AT&T, Verizon, T-Mobile outages: What caused the cell phone outage?

AT&amp;T’s widespread outage that left thousands without service for hours Thursday was likely caused by a process error — not a cyberattack, the carrier said.

AT&T service restored after customers hit by widespread cellular outages in the U.S.

AT&amp;T has yet to say anything about a reported service outage impacting tens of thousands of customers nationwide Thursday morning.

Cellphone outage hits AT&T customers nationwide; Verizon and T-Mobile users also affected | Fox Business

The disruptions left customers potentially unable to contact emergency services.

FBI, FCC investigating AT&T outage that left thousands without cell service across U.S.

More than 70,000 customers have been impacted by outages across AT&amp;T’s network Thursday morning, causing internet and cellular service outages.

What to Know About the Cellular Outages Happening Across the U.S.

Here&#x27;s what to know about the owners of AT&amp;T and other communication companies amid cell phone outages.

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Verizon (VZ) reported earnings 30 days ago. What&#x27;s next for the stock? We take a look at earnings estimates for some clues.

Why Is Verizon (VZ) Down 0.4% Since Last Earnings Report?

All the major MNOs suffered because of an ongoing AT&amp;T outage caused by reasons unknown.

Breaking: AT&T, T-Mobile, Verizon all suffering Outages

Overizon

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Photo by on Sean Parker Unsplash “She is Overconfident”, the sentence has a long history in my life. My dear Anusha teacher from Grade-4, I remember the day you told my parents “Your daughter is OVERCONFIDENT” in the Parent-Teacher meeting. And….. from a conservative background, this meant a negative remark and all along my life I’ve been lectured how over-confidence can kill all my chances of getting better at things. I was accustomed to the fact that being over-confident is my biggest set back. After all these years if I can still remember this, isn’t that talking for itseld? Now that I’m getting a good piece of real professional world I know myself a lot better. Took me a lot of time and a best friend to understand. I’m not complete but I know I’m moving in the right direction. This post is for everyone out there who has ever been told, “YOU ARE OVERCONFIDENT” Save yourself all the thinking, it’s not bad. You’re just underrated and misunderstood. When “Confidence” is perceived attractive, why is “Over Confidence” projected in the dark light. (And that’s what even Google says — guard against overconfidence) means you know more of yourself than anybody else does,at least you’re not among the clueless herd. Overconfidence is not bad, it’s really special. (Not everyone knows themselves). Overconfidence is attractive. There is a lot more in this world than that clueless soul who accused you of this. Be Proud. Overconfidence And every time I started to prove myself for the things I was said I’m overconfident, I changed their perception — That’s the confidence that makes me be happy about who I’m becoming.. My first project at my current company served a major player for this post. Despite me and my kick-ass teammate being super confident about delivering it before schedule, our managers did not share the same optimism. A couple of months down the line, my teammate (who was well experienced) had to go off on sudden long leave. This put the schedule under enormous risk. I’d be lying if I told you that I wasn’t dismayed by this. We had a lot of crunch meetings and me being new to all this was rather shaken up. My boss told us this project has all the signs of impending disaster. But I knew that wasn’t true, I knew my project, I knew my code, I knew Me. They don’t. And yes, we shipped it. And it’s been up and running months now with no issue to very minimal support. The experience was exhilarating. And it only added to my “OVERCONFIDENCE” that I was right. Today I am happy with so many other stories, I still have a long way to go and this time, I’m proud to move forward as an OVERCONFIDENT me. ❤❤ I admire confident souls but I adore overconfident souls. So the next time you see an Overconfident soul, remember you just don’t know enough of them. Also, if you’re the person being called Overconfident, they just don’t know you. Prove them wrong :) Only you know you. Only you know what you’re capable of. Only you are YOU*. Others, can just feel you and call you “OVERCONFIDENT” :D

You are OVERCONFIDENT!

Mental Clarity: Why It's the New Wealth in a World Designed to Confuse You

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What if pollsters are just too afraid of Trump this time around? Out of curiosity about the data-science side of things, in September, I started my own little forecast for the 2024 U.S. presidential election, which considers three different scenarios: ① based on weighted high-quality polls only; ② adjusted by the average polling bias from 2016/20; and ③ adjusted by just half the 2016/20 bias. I’ve previously written about my methodology and predictions here and here. In the latter article, I lay out why I believe another polling bias underestimating Trump is a very real—even probable—possibility that can’t be dismissed. Trump as a candidate seems to completely defy the laws of “normal” elections. It’s unclear how much pollsters have understood and fixed the reasons for their misses in the past 2 elections; the “shy Trump voter” might still exist. On top, Republican-initiated voter suppression—like purging voters from voter rolls in Virginia this close to an election, as recently allowed by the Supreme Court—will disadvantage Democrats in at least some places, which can’t be captured by opinion polling. In the past weeks, however, I’ve also seen arguments as to why things could be the other way ‘round this time, and I must admit that I find some of them quite convincing. Mason McCann argues around campaign-related fundamentals and that Harris has an advantage in every single one of them: money, ground game, vibe, you name it. And it’s true that it seems the Trump campaign can’t really keep up with these. While my take has been that this election is probably most similar to 2016/20 (based on the assumption that you can’t compare elections involving Trump to “normal” elections), McCann argues it might actually be closer to 2012 due to certain similarities: an “enthusiastic” campaign, “perceptions of a bad economy,” and a “very overconfident republican campaign,” among others. Finally, he makes the point that 2024 is different from 2016/20 in that HRC simply didn’t do a lot of campaigning in the Rust Belt, and in 2020, there was no GOTV ground game due to Covid, which, of course, looks very different this year. https://twitter.com/Guntar/status/1851339376196010260?embedable=true So, campaign-related fundamentals point to Harris, and admittedly, this is hard to capture in a purely polling-based forecast. If Dems manage to get their supporters to vote in bigger numbers than in 2016/20—and observations so far suggest this—there might be “a mild surprise when the electorate is not as GOP as the pollsters assume,” writes Dan Guild (@dcg1114 on Twitter). “Pollsters consistently assume a very Republican electorate—my guess is to avoid ’16/’20.” https://twitter.com/dcg1114/status/1851779439899578606?embedable=true Nate Cohn admits as much in his latest article. And @Angry_Staffer, based on this, writes: “Basically, pollsters are terrified of being wrong again, so they’re flat-out ignoring ‘very blue’ results.” https://twitter.com/Angry_Staffer/status/1852421106155540596?embedable=true So, just for funsies, let’s take a look at the case that this year will play out like 2012, shall we? Luckily, Prof. Jacob Long has done a fantastic job analyzing and providing past polling biases on his blog, and that’s what I’ll be working with. I’ll just add the point estimate for the bias to my own polling averages without adjusting my standard deviation. Simply to get a quick, general idea in which direction this would move the forecast. We’re all just 🔮ing here anyway. As a reminder, this is what my current prediction based on weighted high-quality polls only looks like. Michigan, Nevada, North Carolina, and Pennsylvania are toss-ups (according to 538’s definition). Overall, Harris is the ever-so-slight favorite. And when we adjust by the 2012 polling bias, we get this: This would look a lot better for Democrats, but it’s still a relatively close race. Georgia is a toss-up now while Harris has much more comfortable chances in Michigan and Nevada. I’ve also run my Monte Carlo simulation with these probabilities: ④ With 2012 polling bias
===============================
Harris's probability of winning: 71.38%
Trump's probability of winning: 28.20%
Probability of tie: 0.41% While we’re at it, let’s also have a look at how the 2022 polling bias—from the most recent and first “post-Covid” election—would change the predictions: Most significantly here, Michigan, Pennsylvania, and Wisconsin are now lean or likely D. And these are also the ones where Harris is the favorite in all three of the above scenarios. Chances of winning with the 2022 polling bias: ⑤ With 2022 polling bias
===============================
Harris's probability of winning: 66.56%
Trump's probability of winning: 33.10%
Probability of tie: 0.34% So, what are we gonna do with this now? What’s the prediction? Who has the better chance of winning the election, and how good are they? I have no clue whatsoever. As I already said above, it’s all 🔮ing at this point. There are so many variables in this, so many things that could go one or the other way … What we can do at best is think about which scenarios are maybe a bit more likely than others. My opinion on this: Accurate polls (①) and the same polling bias as in 2016/20 (②) are rather unlikely. This leaves ③ (half 2016/20 bias), ④ (2012 bias), and ⑤ (2022 bias) as the more likely of my scenarios. In any case, the election is gonna happen, there’ll be a winner, and there’ll be one option that is closer than the others. All we need is just a little more patience. Please also feel free to head over to my Github—where I'll collect and archive everything—and have a look at the complete data and calculations: https://github.com/maxspeicher/2024-us-presidential-election. I also post regular updates on Bluesky and Twitter. ☕ I love coffee, and if you want to support my work, you can always send me one, or subscribe to my newsletter. 🗞️ What if pollsters are just too afraid of Trump this time around? What if pollsters are just too afraid of Trump this time around? Out of curiosity about the data-science side of things, in September, I started my own little forecast for the 2024 U.S. presidential election, which considers three different scenarios: ① based on weighted high-quality polls only; ② adjusted by the average polling bias from 2016/20; and ③ adjusted by just half the 2016/20 bias. I’ve previously written about my methodology and predictions here and here . own little forecast half here here In the latter article, I lay out why I believe another polling bias underestimating Trump is a very real—even probable—possibility that can’t be dismissed. Trump as a candidate seems to completely defy the laws of “normal” elections. It’s unclear how much pollsters have understood and fixed the reasons for their misses in the past 2 elections; the “shy Trump voter” might still exist. On top, Republican-initiated voter suppression—like purging voters from voter rolls in Virginia this close to an election, as recently allowed by the Supreme Court —will disadvantage Democrats in at least some places, which can’t be captured by opinion polling. recently allowed by the Supreme Court In the past weeks, however, I’ve also seen arguments as to why things could be the other way ‘round this time, and I must admit that I find some of them quite convincing. Mason McCann argues around campaign-related fundamentals and that Harris has an advantage in every single one of them: money, ground game, vibe, you name it. And it’s true that it seems the Trump campaign can’t really keep up with these. While my take has been that this election is probably most similar to 2016/20 (based on the assumption that you can’t compare elections involving Trump to “normal” elections), McCann argues it might actually be closer to 2012 due to certain similarities: an “enthusiastic” campaign, “perceptions of a bad economy,” and a “very overconfident republican campaign,” among others. Mason McCann Finally, he makes the point that 2024 is different from 2016/20 in that HRC simply didn’t do a lot of campaigning in the Rust Belt, and in 2020, there was no GOTV ground game due to Covid, which, of course, looks very different this year. very https://twitter.com/ Guntar /status/1851339376196010260?embedable=true https://twitter.com/ Guntar Guntar /status/1851339376196010260?embedable=true So, campaign-related fundamentals point to Harris, and admittedly, this is hard to capture in a purely polling-based forecast. If Dems manage to get their supporters to vote in bigger numbers than in 2016/20—and observations so far suggest this—there might be “a mild surprise when the electorate is not as GOP as the pollsters assume,” writes Dan Guild ( @dcg1114 on Twitter). “Pollsters consistently assume a very Republican electorate—my guess is to avoid ’16/’20.” @dcg1114 https://twitter.com/dcg1114/status/1851779439899578606?embedable=true https://twitter.com/dcg1114/status/1851779439899578606?embedable=true Nate Cohn admits as much in his latest article . And @Angry_Staffer , based on this, writes: “Basically, pollsters are terrified of being wrong again, so they’re flat-out ignoring ‘very blue’ results.” his latest article @Angry_Staffer https://twitter.com/Angry_Staffer/status/1852421106155540596?embedable=true https://twitter.com/Angry_Staffer/status/1852421106155540596?embedable=true So, just for funsies, let’s take a look at the case that this year will play out like 2012, shall we? Luckily, Prof. Jacob Long has done a fantastic job analyzing and providing past polling biases on his blog , and that’s what I’ll be working with. I’ll just add the point estimate for the bias to my own polling averages without adjusting my standard deviation. Simply to get a quick, general idea in which direction this would move the forecast. We’re all just 🔮ing here anyway. analyzing and providing past polling biases on his blog As a reminder, this is what my current prediction based on weighted high-quality polls only looks like. Michigan, Nevada, North Carolina, and Pennsylvania are toss-ups (according to 538’s definition). Overall, Harris is the ever-so-slight favorite. And when we adjust by the 2012 polling bias, we get this: This would look a lot better for Democrats, but it’s still a relatively close race. Georgia is a toss-up now while Harris has much more comfortable chances in Michigan and Nevada. I’ve also run my Monte Carlo simulation with these probabilities: my Monte Carlo simulation ④ With 2012 polling bias
===============================
Harris's probability of winning: 71.38%
Trump's probability of winning: 28.20%
Probability of tie: 0.41% ④ With 2012 polling bias
===============================
Harris's probability of winning: 71.38%
Trump's probability of winning: 28.20%
Probability of tie: 0.41% While we’re at it, let’s also have a look at how the 2022 polling bias—from the most recent and first “post-Covid” election—would change the predictions: Most significantly here, Michigan, Pennsylvania, and Wisconsin are now lean or likely D. And these are also the ones where Harris is the favorite in all three of the above scenarios. Chances of winning with the 2022 polling bias: ⑤ With 2022 polling bias
===============================
Harris's probability of winning: 66.56%
Trump's probability of winning: 33.10%
Probability of tie: 0.34% ⑤ With 2022 polling bias
===============================
Harris's probability of winning: 66.56%
Trump's probability of winning: 33.10%
Probability of tie: 0.34% So, what are we gonna do with this now? What’s the prediction? Who has the better chance of winning the election, and how good are they? I have no clue whatsoever. As I already said above, it’s all 🔮ing at this point. There are so many variables in this, so many things that could go one or the other way … What we can do at best is think about which scenarios are maybe a bit more likely than others. My opinion on this: Accurate polls (①) and the same polling bias as in 2016/20 (②) are rather unlikely. This leaves ③ (half 2016/20 bias), ④ (2012 bias), and ⑤ (2022 bias) as the more likely of my scenarios. the In any case, the election is gonna happen, there’ll be a winner, and there’ll be one option that is closer than the others. All we need is just a little more patience. Please also feel free to head over to my Github—where I'll collect and archive everything—and have a look at the complete data and calculations: https://github.com/maxspeicher/2024-us-presidential-election . I also post regular updates on Bluesky and Twitter . https://github.com/maxspeicher/2024-us-presidential-election Bluesky Twitter ☕ I love coffee, and if you want to support my work, you can always send me one, or subscribe to my newsletter. 🗞️ ☕ I love coffee, and if you want to support my work, you can always send me one , or subscribe to my newsletter . 🗞️ send me one send me one my newsletter my newsletter

Election Forecasts, Schmelection Forecasts

Surveying the Evolution and Future Trajectory of Generative AI - Evolution of Generative AI

Mr. Altman's Imaginarium: Is the Future Far Away?

Artificial intelligence (AI) has made its way into banking and decentralized finance, so it’s no wonder people are now using it to forecast economic trends. Can it trade stocks better than an expert? Will the federal government look kindly upon its role? While only time can tell, several signs provide insight into those questions. The Difference Between Econometric Models and AI Traditional economic forecasts rely on statistical models and analytics tools. They can't really make predictions unless input data is in a particular, structured format. They’re not data-driven to the extent AI is — few tools can keep pace with its processing speeds. Human intuition is far less accurate than econometric models, but it’s used just as often. Instead of plugging numbers into a spreadsheet and paying attention to market forecasts, people make predictions based on their expertise and gut feelings. These strategies are often inaccurate. Take forecasts on the United States Consumer Price Index, for example. While experts estimated prices would rise by 7.5% in 2022, they actually increased by 6.5%, according to the U.S. Bureau of Labor Statistics. AI outperforms econometric models and human intuition. It can interpret complex relationships between variables, even considering outliers on a weighted scale. Also, it mitigates loss aversion, overconfidence, and the gambler’s fallacy, preventing biases from skewing forecasts. AI enables automation, so it can execute trades, adjust prices, or transfer funds at the most advantageous times. Naturally, it also reduces administrative work. According to one estimate, it could automate up to 70% of the workday today. Examples of AI Techniques Used to Forecast Trends Whatever AI technique a person uses influences their forecast generation approach. With machine learning, they can uncover nonlinear relationships between variables. These models can __process complex data in milliseconds __and update it whenever they receive new information. A neural network uses a series of interconnected nodes to recognize patterns. It’s inspired by the human brain — the artificial neurons work together in real-time to solve problems. As a result, its forecasts are precise and reasonable. With natural language processing, someone can process text-based data to make forecasts. This system can also analyze sentiment to determine tone and mood. Instead of pressing buttons, updating a spreadsheet, or entering code, the user can use plain language to generate output. Image recognition works similarly, analyzing images and videos to reveal hidden patterns or forecast trends. For example, when leveraged in an AI-powered surveillance system, it could evaluate foot traffic and purchase frequency to predict the following month’s sales figures. The Implications of AI Forecasts on Various Sectors Naturally, the finance sector will be most impacted by AI-driven economic forecasts. It could automate stock trading, predict inflation, and estimate growth rates with unparalleled accuracy. However, no technology is infallible — an algorithm simply doesn’t have the expertise of an industry professional, even if it has access to massive amounts of data. A good hypothetical is necessary to explain. During the non-fungible token craze, an algorithm may have misinterpreted the fad as a valuable emerging alternative investment. It couldn’t have understood the general public’s negative sentiment, which would’ve skewed its findings. The same situation could happen in e-commerce, where forecasts are vital for knowing how much to order and when to change prices. On one hand, AI could align retailers’ actions with customer demand, improving brand loyalty. Conversely, it might misread the situation because of incomplete information, leading to poor business outcomes. Whether the implications are more positive or negative, big change is coming. As a result, government policies may evolve. They’ll likely target data privacy and copyright instead of policing the act of forecasting. The Senate Task Force on AI has passed at least 15 bills to date, with more on the way. It probably won’t be nearly as strict as European lawmakers. However, any privacy and copyright laws limit what data companies can use in training and forecasting, which would reduce model accuracy right off the bat. Will AI Become the Standard for Forecasting Trends? Since AI is relatively new, it likely won’t replace industry experts and professional stock traders anytime soon. However, whether its hype continues, it will likely have a permanent place in forecasting since it enables automation and rapid insight generation. Artificial intelligence (AI) has made its way into banking and decentralized finance, so it’s no wonder people are now using it to forecast economic trends. Can it trade stocks better than an expert? Will the federal government look kindly upon its role? While only time can tell, several signs provide insight into those questions. The Difference Between Econometric Models and AI Traditional economic forecasts rely on statistical models and analytics tools. They can't really make predictions unless input data is in a particular, structured format. They’re not data-driven to the extent AI is — few tools can keep pace with its processing speeds. rely on statistical models rely on statistical models Human intuition is far less accurate than econometric models, but it’s used just as often. Instead of plugging numbers into a spreadsheet and paying attention to market forecasts, people make predictions based on their expertise and gut feelings. These strategies are often inaccurate. Take forecasts on the United States Consumer Price Index, for example. While experts estimated prices would rise by 7.5% in 2022, they actually increased by 6.5% , according to the U.S. Bureau of Labor Statistics. would rise by 7.5% would rise by 7.5% actually increased by 6.5% actually increased by 6.5% AI outperforms econometric models and human intuition. It can interpret complex relationships between variables, even considering outliers on a weighted scale. Also, it mitigates loss aversion, overconfidence, and the gambler’s fallacy, preventing biases from skewing forecasts. AI enables automation, so it can execute trades, adjust prices, or transfer funds at the most advantageous times. Naturally, it also reduces administrative work. According to one estimate, it could automate up to 70% of the workday today. up to 70% of the workday up to 70% of the workday Examples of AI Techniques Used to Forecast Trends Whatever AI technique a person uses influences their forecast generation approach. With machine learning, they can uncover nonlinear relationships between variables. These models can __ process complex data in milliseconds __and update it whenever they receive new information. process complex data in milliseconds A neural network uses a series of interconnected nodes to recognize patterns. It’s inspired by the human brain — the artificial neurons work together in real-time to solve problems. As a result, its forecasts are precise and reasonable. With natural language processing, someone can process text-based data to make forecasts. This system can also analyze sentiment to determine tone and mood. Instead of pressing buttons, updating a spreadsheet, or entering code, the user can use plain language to generate output. Image recognition works similarly, analyzing images and videos to reveal hidden patterns or forecast trends. For example, when leveraged in an AI-powered surveillance system, it could evaluate foot traffic and purchase frequency to predict the following month’s sales figures. The Implications of AI Forecasts on Various Sectors Naturally, the finance sector will be most impacted by AI-driven economic forecasts. It could automate stock trading, predict inflation, and estimate growth rates with unparalleled accuracy. However, no technology is infallible — an algorithm simply doesn’t have the expertise of an industry professional, even if it has access to massive amounts of data. A good hypothetical is necessary to explain. During the non-fungible token craze, an algorithm may have misinterpreted the fad as a valuable emerging alternative investment. It couldn’t have understood the general public’s negative sentiment, which would’ve skewed its findings. The same situation could happen in e-commerce, where forecasts are vital for knowing how much to order and when to change prices. On one hand, AI could align retailers’ actions with customer demand, improving brand loyalty. Conversely, it might misread the situation because of incomplete information, leading to poor business outcomes. Whether the implications are more positive or negative, big change is coming. As a result, government policies may evolve. They’ll likely target data privacy and copyright instead of policing the act of forecasting. The Senate Task Force on AI has passed at least 15 bills to date, with more on the way. It probably won’t be nearly as strict as European lawmakers. However, any privacy and copyright laws limit what data companies can use in training and forecasting, which would reduce model accuracy right off the bat. passed at least 15 bills passed at least 15 bills Will AI Become the Standard for Forecasting Trends? Since AI is relatively new, it likely won’t replace industry experts and professional stock traders anytime soon. However, whether its hype continues, it will likely have a permanent place in forecasting since it enables automation and rapid insight generation.

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