ZDNet reports an interesting static from Microsoft’s CEO Satya Nadella. “We have over 1 million paid Copilot users in more than 37,000 organizations that subscribe to Copilot for business, with significant traction outside the United States.”
And Microsoft’s quarterly results also “showed early signs that the company’s investments in generative AI were beginning to bolster sales, most notably reversing what had been slowing growth of the company’s important cloud computing product,” reports the New York Times. (Alternate URL here.)
The company had $56.5 billion in sales in the three months that ended in September, up 13% from a year earlier. Profit hit $22.3 billion, up 27%. The results beat analyst expectations and Microsoft’s own estimates.
Microsoft had told investors that A.I. wouldn’t start producing meaningful results until after the start of 2024, when more products became widely available. The company and its competitors are racing to put generative A.I. into nearly every product they offer. Microsoft is seen by many companies as a leading A.I. provider, thanks to its partnership with — and $13 billion investment in — the start-up OpenAI, which introduced the chatbot ChatGPT almost a year ago. Microsoft’s flagship cloud computing product, Azure, grew 29%, up from 26% in the previous quarter. About three percentage points of Azure’s growth came from generative A.I. products, including the access Microsoft provides to OpenAI’s GPT-4 language model, more than the company had told investors to expect.
More than 18,000 organizations are using Microsoft’s Azure OpenAI services, Satya Nadella, the company’s chief executive, said in a call with investors. He said that included customers who had not used Azure before. “Azure again took share as organizations took their workloads to our cloud,” Mr. Nadella said… The company said that sales could increase as much as 8.7% in the current quarter, exceeding investor expectations, and that it was investing in building data centers to support the demand for A.I. and cloud computing…
Microsoft’s personal computing business grew just 3%, to $13.7 billion, reflecting how consumer behaviors have shifted since the laptop-buying binges of the pandemic. The revenue of the Windows operating system installed on new computers was up 4%. Gaming provided a consumer bright spot, with Xbox content and services up 13%.
Next month Microsoft integrates its Copilot AI product into its Excel/Word/Teams “productivity suite” — but Microsoft CEO Satya Nadella said that 40% of Fortune 100 companies have already been testing the feature during its “limited preview”, and “so far, so good.”
Yet the article notes it isn’t all good news for Microsoft. Investment bank UBS has told investors that while Microsoft integrated an AI-powered chatbot into its Bing search engine, there is “no evidence” that Bing has actually gained any search market share.
DietPi v8.23 which was released this past week, introduced a variety of enhancements, specifically targeting devices like Raspberry Pi, ROCK5B, ROCK 4, and VisionFive 2, alongside several notable improvements and many bug fixes. DietPi: DietPi is a lightweight and optimized OS based on Linux, specifically designed for single-board computers and small-scale devices. It aims to […]
Apple’s 9th generation iPad is $80 off at Amazon right now. The discount brings the 64GB variant down to just $250 from its regular price of $330, a record low typically only seen on Prime Day. You can also snag the 9th-gen iPad with 256GB of storage for $80 off at Amazon, where it’s currently down to $400 from its usual $480.
The 9th-gen iPad came out in 2021, but it’s still a solid tablet especially if you’re on a budget. While its A13 Bionic chip isn’t the fastest or most powerful, it’s more than enough for basic productivity tasks, browsing and streaming. It earned a score of 86 when we reviewed it back at the time of its release, and it’s still one of the best iPads you can get that won’t break the bank.
It has a heftier build than the newer, sleeker models, with chunky bezels framing its 10.2-inch Retina Display, and a physical Home button with Touch ID. Apple’s 9th-gen iPad also still has a headphone jack and charges via lightning port. It has a 12MP ultrawide front camera and 8MP back camera, and supports Apple’s Center Stage video calling feature.
The 9th generation iPad comes in Silver and Space Gray, and the discount applies to both color variants for the Wi-Fi only model. It’s a great option for the casual iPad user, and the price right now can’t be beat. But, if those specs aren’t quite cutting it, Amazon is also running a deal on the 10th generation iPad, which is a step up. That model is currently $50 off.
This article originally appeared on Engadget at https://www.engadget.com/apples-9th-gen-ipad-is-back-to-its-all-time-low-price-of-250-ahead-of-black-friday-154710678.html?src=rss
Bloomberg reports that Comcast’s stock price took its biggest drop in over a year on Thursday, “after reporting drops in broadband and cable subscribers, and predicting more losses to come.”
Cord-cutting and increasing competition have eroded Comcast’s traditional customer base. The company, which owns Xfinity, the NBCUniversal media empire and SkyTV, lost 490,000 cable-TV customers in the third quarter, better than analysts expected but part of an ongoing trend as consumers switch to streaming services like Netflix. It also lost 18,000 broadband subscribers in the quarter, with nearly all of those residential customers. Analysts had predicted Comcast would instead gain 10,900 residential broadband customers.
Shares fell as much as 8% on the news Thursday, their biggest intraday decline since July 2022.
“Growth has halted for Comcast — the largest US broadband provider, with 32 million homes,” said Bloomberg Intelligence senior media analyst Geetha Ranganathan. “The company derives 80% of profit from cable, where, even after a pandemic-demand surge, broadband has been hurt by fierce competition and low-move activity among customers.” Comcast expects “somewhat higher subscriber losses” in the fourth quarter due to pullback on promotional offers that targeted lower-end customers, Chief Financial Officer Jason Armstrong said on a call with investors. Revenue per customer climbed, however, in part because of price increases and promotions of higher-rate plans.
Broadband is becoming increasingly competitive as mobile providers move into the market with improved wireless internet offerings. In the past week, the Big Three — T-Mobile US Inc., AT&T Inc. and Verizon Communications Inc. — all reported subscriber gains.
In addition to visible signs of wear, pieces of leather furniture that are frequently in use—like a living room couch or office chair—may start to wrinkle over time. Known as “comfort wrinkles,” they can range from gentle rippling, to larger creases, and probably aren’t the look you were going for when you decided to…
The SF Standard reports that a San Francisco man whose car was stolen in the middle of the night “managed to track down the vehicle using his car insurance app and retrieve the stolen vehicle the following morning within half an hour of noticing it was gone.”
Harris realized he could track his phone using his app from MetroMile, a San Francisco-based digital pay-per-mile car insurance company that tracks a car’s location and charges a rate based on how much it’s driven. “I opened the app and found it was in Mission Bay,” he said, adding that the person who stole it drove it all night before parking. “I rode my bike down there and picked it up….”
Before picking up his car, Harris didn’t consult with the San Francisco Police Department and said officers were confused about why he wanted to report a stolen car that was already back in his possession. He said his driver’s side window had been smashed, but there wasn’t any other damage, just a mess of marijuana paraphernalia and blunt wraps inside… “If a vehicle owner locates their stolen vehicle prior to the police locating it, we highly recommend that they alert us to the vehicle’s location and do not move the car prior to reporting it recovered,” Sgt. Kathryn Winters wrote in an email. “Additionally, if they locate the vehicle occupied, they should not approach the vehicle or suspects and should call law enforcement immediately.”
There were 274 motor vehicle theft reports in the Western Addition neighborhood, which includes Alamo Square, in the 12 months leading up to Oct. 21 compared with 219 during the same period the previous year, according to police data. Citywide, the problem has also gotten worse in recent years. The number of car thefts has risen from 60 incidents per 10,000 residents in 2019 to 101 incidents this year.
The explosive growth in artificial intelligence in recent years — crowned with the meteoric rise of generative AI chatbots like ChatGPT — has seen the technology take on many tasks that, formerly, only human minds could handle. But despite their increasingly capable linguistic computations, these machine learning systems remain surprisingly inept at making the sorts of cognitive leaps and logical deductions that even the average teenager can consistently get right.
Focusing on the five evolutionary “breakthroughs,” amidst myriad genetic dead ends and unsuccessful offshoots, that led our species to our modern minds, Bennett also shows that the same advancements that took humanity eons to evolve can be adapted to help guide development of the AI technologies of tomorrow. In the excerpt below, we take a look at how generative AI systems like GPT-3 are built to mimic the predictive functions of the neocortex, but still can’t quite get a grasp on the vagaries of human speech.
GPT-3 is given word after word, sentence after sentence, paragraph after paragraph. During this long training process, it tries to predict the next word in any of these long streams of words. And with each prediction, the weights of its gargantuan neural network are nudged ever so slightly toward the right answer. Do this an astronomical number of times, and eventually GPT-3 can automatically predict the next word based on a prior sentence or paragraph. In principle, this captures at least some fundamental aspect of how language works in the human brain. Consider how automatic it is for you to predict the next symbol in the following phrases:
One plus one equals _____
Roses are red, violets are _____
You’ve seen similar sentences endless times, so your neocortical machinery automatically predicts what word comes next. What makes GPT-3 impressive, however, is not that it just predicts the next word of a sequence it has seen a million times — that could be accomplished with nothing more than memorizing sentences. What is impressive is that GPT-3 can be given a novel sequence that it has never seen before and still accurately predict the next word. This, too, clearly captures something that the human brain can _____.
Could you predict that the next word was do? I’m guessing you could, even though you had never seen that exact sentence before. The point is that both GPT-3 and the neocortical areas for language seem to be engaging in prediction. Both can generalize past experiences, apply them to new sentences, and guess what comes next.
GPT-3 and similar language models demonstrate how a web of neurons can reasonably capture the rules of grammar, syntax, and context if it is given sufficient time to learn. But while this shows that prediction is part of the mechanisms of language, does this mean that prediction is all there is to human language? Try to finish these four questions:
If 3x + 1 = 3, then x equals _____
I am in my windowless basement, and I look toward the sky, and I see _____
He threw the baseball 100 feet above my head, I reached my hand up to catch it, jumped, and _____
I am driving as fast as I can to LA from New York. One hour after passing through Chicago, I finally _____
Here something different happens. In the first question, you likely paused and performed some mental arithmetic before being able to answer the question. In the other questions, you probably, even for only a split second, paused to visualize yourself in a basement looking upward, and realized what you would see is the ceiling. Or you visualized yourself trying to catch a baseball a hundred feet above your head. Or you imagined yourself one hour past Chicago and tried to find where you would be on a mental map of America. With these types of questions, more is happening in your brain than merely the automatic prediction of words.
We have, of course, already explored this phenomenon—it is simulating. In these questions, you are rendering an inner simulation, either of shifting values in a series of algebraic operations or of a three-dimensional basement. And the answers to the questions are to be found only in the rules and structure of your inner simulated world.
I gave the same four questions to GPT-3; here are its responses (responses of GPT-3 are bolded and underlined):
If 3x + 1 = 3 , then x equals 1
I am in my windowless basement, and I look toward the sky, and I see a light, and I know that it is a star, and I am happy.
He threw the baseball 100 feet above my head, I reached my hand up to catch it, jumped, and caught it. It was a lot of fun!
I am driving as fast as I can to LA from New York. One hour after passing through Chicago, I finally get to see the Pacific Ocean.
All four of these responses demonstrate that GPT-3, as of June 2022, lacked an understanding of even simple aspects of how the world works. If 3x + 1 = 3, then x equals 2/3, not 1. If you were in a basement and looked toward the sky, you would see your ceiling, not stars. If you tried to catch a ball 100 feet above your head, you would not catch the ball. If you were driving to LA from New York and you’d passed through Chicago one hour ago, you would not yet be at the coast. GPT-3’s answers lacked common sense.
What I found was not surprising or novel; it is well known that modern AI systems, including these new supercharged language models, struggle with such questions. But that’s the point: Even a model trained on the entire corpus of the internet, running up millions of dollars in server costs — requiring acres of computers on some unknown server farm — still struggles to answer common sense questions, those presumably answerable by even a middle-school human.
Of course, reasoning about things by simulating also comes with problems. Suppose I asked you the following question:
Tom W. is meek and keeps to himself. He likes soft music and wears glasses. Which profession is Tom W. more likely to be?
1) Librarian
2) Construction worker
If you are like most people, you answered librarian. But this is wrong. Humans tend to ignore base rates—did you consider the base number of construction workers compared to librarians? There are probably one hundred times more construction workers than librarians. And because of this, even if 95 percent of librarians are meek and only 5 percent of construction workers are meek, there still will be far more meek construction workers than meek librarians. Thus, if Tom is meek, he is still more likely to be a construction worker than a librarian.
The idea that the neocortex works by rendering an inner simulation and that this is how humans tend to reason about things explains why humans consistently get questions like this wrong. We imagine a meek person and compare that to an imagined librarian and an imagined construction worker. Who does the meek person seem more like? The librarian. Behavioral economists call this the representative heuristic. This is the origin of many forms of unconscious bias. If you heard a story of someone robbing your friend, you can’t help but render an imagined scene of the robbery, and you can’t help but fill in the robbers. What do the robbers look like to you? What are they wearing? What race are they? How old are they? This is a downside of reasoning by simulating — we fill in characters and scenes, often missing the true causal and statistical relationships between things.
It is with questions that require simulation where language in the human brain diverges from language in GPT-3. Math is a great example of this. The foundation of math begins with declarative labeling. You hold up two fingers or two stones or two sticks, engage in shared attention with a student, and label it two. You do the same thing with three of each and label it three. Just as with verbs (e.g., running and sleeping), in math we label operations (e.g., add and subtract). We can thereby construct sentences representing mathematical operations: three add one.
Humans don’t learn math the way GPT-3 learns math. Indeed, humans don’t learn language the way GPT-3 learns language. Children do not simply listen to endless sequences of words until they can predict what comes next. They are shown an object, engage in a hardwired nonverbal mechanism of shared attention, and then the object is given a name. The foundation of language learning is not sequence learning but the tethering of symbols to components of a child’s already present inner simulation.
A human brain, but not GPT-3, can check the answers to mathematical operations using mental simulation. If you add one to three using your fingers, you notice that you always get the thing that was previously labeled four.
You don’t even need to check such things on your actual fingers; you can imagine these operations. This ability to find the answers to things by simulating relies on the fact that our inner simulation is an accurate rendering of reality. When I mentally imagine adding one finger to three fingers, then count the fingers in my head, I count four. There is no reason why that must be the case in my imaginary world. But it is. Similarly, when I ask you what you see when you look toward the ceiling in your basement, you answer correctly because the three-dimensional house you constructed in your head obeys the laws of physics (you can’t see through the ceiling), and hence it is obvious to you that the ceiling of the basement is necessarily between you and the sky. The neocortex evolved long before words, already wired to render a simulated world that captures an incredibly vast and accurate set of physical rules and attributes of the actual world.
To be fair, GPT-3 can, in fact, answer many math questions correctly. GPT-3 will be able to answer 1 + 1 =___ because it has seen that sequence a billion times. When you answer the same question without thinking, you are answering it the way GPT-3 would. But when you think about why 1 + 1 =, when you prove it to yourself again by mentally imagining the operation of adding one thing to another thing and getting back two things, then you know that 1 + 1 = 2 in a way that GPT-3 does not.
The human brain contains both a language prediction system and an inner simulation. The best evidence for the idea that we have both these systems are experiments pitting one system against the other. Consider the cognitive reflection test, designed to evaluate someone’s ability to inhibit her reflexive response (e.g., habitual word predictions) and instead actively think about the answer (e.g., invoke an inner simulation to reason about it):
Question 1: A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball. How much does the ball cost?
If you are like most people, your instinct, without thinking about it, is to answer ten cents. But if you thought about this question, you would realize this is wrong; the answer is five cents. Similarly:
Question 2: If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets?
Here again, if you are like most people, your instinct is to say “One hundred minutes,” but if you think about it, you would realize the answer is still five minutes.
And indeed, as of December 2022, GPT-3 got both of these questions wrong in exactly the same way people do, GPT-3 answered ten cents to the first question, and one hundred minutes to the second question.
The point is that human brains have an automatic system for predicting words (one probably similar, at least in principle, to models like GPT-3) and an inner simulation. Much of what makes human language powerful is not the syntax of it, but its ability to give us the necessary information to render a simulation about it and, crucially, to use these sequences of words to render the same inner simulation as other humans around us.
This article originally appeared on Engadget at https://www.engadget.com/hitting-the-books-a-brief-history-of-intelligence-max-bennett-mariner-books-143058118.html?src=rss
David Fincher is known for a number of films and TV shows, though his 2011 adaptation of The Girl With the Dragon Tattoo isn’t one that gets talked about as heavily. With widespread critical acclaim and an Oscar nomination for Rooney Mara (along with a still pretty great opening title sequence), the movie mainly…
Google Maps is getting infused with more AI features to make using the app more intuitive when planning and navigating trips. The new features aim to help users make more eco-friendly choices, while inspiring more things to do along the journey.
During its I/O event earlier this year, Google announced that it would launch Immersive View
The Xiph.Org-developed Theora lossy video compression format was once popular for open-source video compression but in an era of VP9 and AV1 its usage has waned. Google engineers are now working to remove Theora support from their Chrome/Chromium web browser…
Akihabara News (Tokyo) — Recent moves by the National Police Agency suggest that the long-rumored crackdown on online gambling may finally be underway.
The strongest move came late last month when police made unprecedented arrests of five alleged settlement agents for online casinos based overseas, in this case the Netherlands.
According to the information which police released to the media, the suspects made over US$14 million in commissions on hundreds of millions of dollars sent to the overseas online casino operators. In other words, they were the financial middlemen who collected deposits from Japanese gamblers and then ensured that the foreign company or companies were paid for the customers’ gambling losses.
The investigators said that about 42,000 names were registered in the customer data of the arrested agents. Also, a couple dozen gamblers were charged with simple gambling.
Earlier in September, a man in Chiba Prefecture was arrested for posting videos of himself on YouTube as he was engaged in online gambling. One of the main problems with enforcement is the fact this crime usually takes place quietly in peoples’ private homes, but in this case the man broadcast his lawbreaking to the public.
The National Police Agency is eager to dispel the widespread notion that online gambling is a legal “gray area.” In fact, since 2013 it has been explicitly designated as a crime–at least for those who physically operate within Japan. In other words, Japanese law does not apply to the foreign online casino operators, but it does apply to anyone inside Japan who assists these operations–such as the financial settlement agents–as well as ordinary people who utilize these gambling services (which are marketed and operated in Japanese language).
There are thought to be nearly 3 million Japanese who sometimes engage in illegal online gambling.
In order to catch some of them–or least some of those who facilitate online casinos–from this month the National Police Agency is expanding the scope of its anonymous crime reporting system to include gambling offenses.
Whether Halloween is a hectic day in your household—getting people in costume, making sure the candy bowl is stocked, and putting the finishing touches on your spooky outdoor decor—or it’s just another day for you, you’re going to have to eat at some point.
One of the many early pull requests sent in for Linux 6.7 were the x86/boot changes that are headlined by a rework to the PE header generation in order to generate a modern, 4K-aligned kernel image view to ultimately aim for better system security…
This year’s release of the world’s most popular operating system feels like one of the smallest ever, bringing just a handful of new features. Even during the Android portion of Google’s big I/O keynote, Google spent most of its time showing off a new generative AI feature that creates wallpapers for you, as if there aren’t enough wallpapers in the world.
Last year’s Android 13 release felt small, but that was because it was the second major Android OS release that year. Android 12L—the big tablet and foldable release—came out earlier. What’s Android 14’s excuse? We’re not really sure. We still have a few things to go over, though, like new lock screen customizations, genuinely exciting changes to the way the back button works, and a pile of under-the-hood changes.
To explore America’s “transition to a post-quantum world,” the Washington Post interviewed U.S. federal official Nick Polk, who is focused on national security issues including quantum computing and is also a senior advisor to a White House federal chief information security officer):
The Washington Post: The U.S. is in the early stages of a major shift focused on bolstering government network defenses, pushing federal agencies to adopt a new encryption standard known as post-quantum cryptography that aims to prevent systems from being vulnerable to advanced decryption techniques enabled by quantum computers in the near future…
Nick Polk: We’ve been using asymmetric encryption for a very long time now, and it’s been ubiquitous since about 2014, when the U.S. government and some of the large tech companies decided that they’re going to make it a default on most web browsers… Interestingly enough, regarding the post-quantum cryptographic standards being developed, the only thing that’s quantum about them is that it has “quantum” in the name. It’s really just a different type of math that’s much more difficult for a quantum computer to be able to reverse-engineer. The National Institute of Standards and Technology is looking at different mathematical models to cover all their bases. The interesting thing is that these post-quantum standards are actually being used to protect classical computers that we have now, like laptops…
Given the breadth of the U.S. government and the amount of computing power we use, we really see ourselves and our role as a steward of the tech ecosystem. One of the things that came out of [this week’s Inside Quantum Technology conference in New York City] was that we are very quickly moving along with the private sector to migrate to post-quantum cryptography. I think you’re gonna see very shortly a lot of very sensitive private sector industries start to migrate or start to advertise that they’re going to migrate. Banks are a perfect example. That means meeting with vendors regularly, and testing their algorithms to ensure that we can accurately and effectively implement them on federal systems…
The administration and national security memorandum set 2035 as our deadline as a government to migrate our [national security] systems to post-quantum cryptography. That’s supposed to time with the development of operational quantum computers. We need to ensure that we start now, so that we don’t end up not meeting the deadline before computers are operational… This is a prioritized migration for the U.S. government. We’re going to start with our most critical systems — that includes what we call high-value assets, and high-impact systems. So for example, we’re gonna prioritize systems that have personal health information.
That’s our biggest emphasis — both when we talk to private industry and when we encourage agencies when they talk to their contractors and vendors — to really think about where your most sensitive data is and then prioritize those systems for migration.
More than 1.5 billion years ago, a momentous thing happened: Two small, primitive cells became one. Perhaps more than any event—barring the origin of life itself—this merger radically changed the course of evolution on our planet.
One cell ended up inside the other and evolved into a structure that schoolkids learn to refer to as the “powerhouse of the cell”: the mitochondrion. This new structure provided a tremendous energetic advantage to its host—a precondition for the later evolution of complex, multicellular life.
But that’s only part of the story. The mitochondrion is not the only important structure within complex, eukaryotic cells. There’s the membrane-bound nucleus, safekeeper of the genome. There’s a whole system of internal membranes: the endoplasmic reticulum, the Golgi apparatus, lysosomes, peroxisomes and vacuoles—essential for making, transporting, and recycling proteins and other cargo in and around the cell.
While the Linux 6.6 kernel isn’t set to be released until later today, over the weekend a number of early pull requests were submitted of new material for the Linux 6.7 merge window. Among those early PRs were for the Error Detection and Correction (EDAC) area of the kernel…
Virtualization has become a cornerstone of modern computing, offering a myriad of benefits from cost savings to improved efficiency and scalability. Among the myriad of options available for server virtualization, Ubuntu Server stands out as a powerful and versatile choice. In this comprehensive guide, we’ll delve into the intricacies of using Ubuntu Server for virtualization, focusing specifically on two critical tools: Kernel-based Virtual Machine (KVM) and Quick Emulator (QEMU).
The creator economy is probably bigger than you think. The Washington Post reports it’s “now a global industry valued at $250 billion, with tens of millions of workers, hundreds of millions of customers and its own trade association and work-credentialing programs.”
Millions have ditched traditional career paths to work as online creators and content-makers, using their computers and phones to amass followers and build businesses whose influence now rivals the biggest names in entertainment, news and politics… In the United States, the video giant YouTube estimated that roughly 390,000 full-time jobs last year were supported by its creators’ work — four times the number of people employed by General Motors, America’s biggest automaker…
This spring, analysts at Goldman Sachs said that 50 million people now work as creators around the world. The analysts expect the industry’s “total addressable market,” an estimate of consumer demand, will jump from $250 billion this year to $480 billion by 2027. For comparison, the global revenue from video games, now at about $227 billion, is expected to climb to roughly $312 billion by 2027, analysts at the financial giant PwC estimated in June. YouTube’s report estimated that its creators contributed $35 billion to [U.S.] gross domestic product last year, a figure that would rank the group’s combined output ahead of U.S. furniture manufacturing but behind rail transportation, according to industry data from the U.S. Bureau of Economic Analysis….
Payments from advertisers to creators in the United States have more than doubled since 2019, to $5 billion, estimates from the market research firm Insider Intelligence show… Megan Pollock, a branding executive at Panasonic North America, said that the company now devotes about 10 percent of its marketing budget to creators and that she expects further increases amid a long-term shift away from traditional ad campaigns.
Other interesting details from the article:
Last month people watched 53 million hours of video a day just on Twitch. But 74% of that went to the top 10,000 streamers (according to data from the analytics firm StreamElements).
“Creators’ incomes are determined by giant tech and advertising companies that can change the rules in an instant, and a single mistake can unravel their careers.”
When America’s youth are asked what they want to be when they grow up, “Influencer” is now one of the most popular answers — ranking higher than “astronaut” and “professional athlete”
