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reduce the size of Coinbase by ~14%

By the fourth quarter of 2025, the five largest chip designers had cumulatively shipped roughly 20 million AI chips

Liam Price just cracked a 60-year-old problem that world-class mathematicians have tried and failed to solve. He's 23 years old and has no advanced mathematics training.

More than 3,000 forensic engines run in parallel on every submitted sample, covering signal, prosody, articulation, codec, and provenance domains.

The FBI Internet Crime Complaint Center logged 2.3 billion dollars in losses for victims aged 60 and over in calendar year 2026.

Pindrop reported a 475 percent year-over-year increase in synthetic voice attacks against insurance call centers across 2025.

The Wall Street Journal reported in February 2026 that high-quality voice cloning now requires roughly fifteen seconds of clean reference audio for tools available off the shelf.

According to the leaked sample index, the archive covers more than 40,000 contractors who signed up to label data, record reading passages, and run through verification calls for AI training.

The dump is reported at roughly four terabytes and bundles a payload that breach analysts have been warning about for two years: voice biometrics paired with the same person's government-issued identity document.

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Founded at the Massachusetts Institute of Technology in 1899

That momentum is starting to extend beyond engineering. Teams are using Codex to pull together context from different tools, reason through what matters, and turn scattered information into useful work - like briefs, plans, checklists, drafts, and follow-ups.

Our professionals are using Codex to move from static requirements to working solutions in hours, not weeks. It's enabling rapid prototyping, real-time workflow redesign, and faster iteration across the development lifecycle.

Companies are using Codex across the software development lifecycle. Virgin Atlantic is using it to increase test coverage and increase team velocity - reducing technical debt and improving performance.

🔹 **DeepSeek-V4-Pro:** 1.6T total / 49B active params. Performance rivaling the world's top closed-source models.

Ubuntu 26.04 LTS provides the strongest foundation for our confidential computing stack. It allows us to deploy a single securely designed image for all our verifiably private AI workloads across Intel, AMD, and NVIDIA hardware, with no platform-specific changes required.

Ubuntu now fully supports RVA23, the baseline standard for RISC-V. This ensures that teams innovating on RISC-V can take full advantage of the platform, including in mixed-architecture environments.

TPM-backed full-disk encryption is now generally available in the Ubuntu installer.

Ubuntu 26.04 LTS is the first LTS to expand the number of memory safe system components. In practice, this means new kernel drivers and subsystems written in Rust, as well as `sudo-rs` and `uutils``coreutils` bringing memory-safe reimplementations of foundational system tools such as `sudo`, `ls`, `cp`, and `mv`.

Canonical Livepatch now extends its rebootless kernel patching capability to Arm64 for the first time.

IgH Master driver brings microsecond-level timing precision natively into the OS, removing a significant integration burden for engineers building motion control systems, robotics platforms, or complex factory automation.

Ubuntu 26.04 LTS is built on Linux 7.0, continuing Canonical's commitment to shipping the latest upstream kernels at the time of release.

With optimized images across AWS, Azure, Google Cloud, IBM Cloud and Oracle Cloud, developers and enterprises can rely on Ubuntu 26.04 LTS for their most demanding public cloud workloads.

Ubuntu powers millions of PCs and laptops around the world.

The 11th long-term supported release of Ubuntu delivers deep silicon optimization and state-of-the-art security for enterprise workloads.

Each cell shows how often a given curve fit is not significantly worse than the fit with the best cross-validation accuracy.

We examine whether AI capabilities are accelerating by fitting statistical models to benchmark performance over time, and comparing their predictive accuracies.

Three of four metrics show strong evidence of acceleration, driven by reasoning models.

Our fourth metric, an index constructed from WeirdML V2 results, showed no sign of acceleration. A single global linear trend fit the data best.

The best-performing model across these three metrics was a pair of independent linear trends: one for reasoning models and one for non-reasoning models.

Reasoning models show both a one-off jump in performance and a roughly 2-3x faster trend compared to non-reasoning models.

Three of the four metrics (ECI, log METR 50% time horizon, and a math-focused index we constructed from several math benchmarks) show strong evidence that progress has sped up relative to a global linear trend fit to data from 2023 onward.

Three of the four metrics (ECI, log METR 50% time horizon, and a math-focused index we constructed from several math benchmarks) show strong evidence that progress has sped up relative to a global linear trend fit to data from 2023 onward.

The three metrics where we find acceleration are concentrated in programming and mathematics. These are areas that labs have explicitly targeted for improvement

Our fourth metric, an index constructed from WeirdML V2 results, showed no sign of acceleration. A single global linear trend fit the data best.

The best-performing model across these three metrics was a pair of independent linear trends: one for reasoning models and one for non-reasoning models.

Reasoning models show both a one-off jump in performance and a roughly 2-3x faster trend compared to non-reasoning models.

Three of four metrics show strong evidence of acceleration, driven by reasoning models.

We work with the natural logarithm of the time horizon, which puts it on an approximately linear scale.

Three of four metrics show strong evidence of acceleration, driven by reasoning models.

Meta founder and CEO Mark Zuckerberg described superintelligence in a blog post last year

Wedbush Securities analyst Dan Ives said in a report on Thursday.

Meta plans to lay off roughly 8,000 employees, or 10% of its workforce

The median US buyout fund returns 13% to 16% net.

Claude usage rose by over 40% amid increased attention but remains far behind ChatGPT

The proof box defines an ancient formula first used by the Greeks: at-bats + walks + sacrifices + hit batsmen + interference calls = runs + putouts + runners stranded. If these two sums are not equal, go find your mistake. (The sum should also be the number of the last at-bat box used.)

nearly half of the workers who received sloppy AI-generated material from coworkers saw those coworkers as less creative, capable and reliable, while smaller amounts saw them as less trustworthy and intelligent.

The cost of the time that it takes fix "workslop" could add up too, with a $186 monthly cost per employee on average, according to a survey of desk workers by BetterUp in partnership with the Stanford Social Media Lab. Forty percent of the workers surveyed said they received "workslop" in the last month and that it took an average of two hours to resolve each incident.

About 70% of managers say their direct reports have made AI-related mistakes in the past year

58% said direct reports submitted work that contained factual inaccuracies generated by AI tools, while fewer reported that AI failed to account for critical contextual factors. Other issues cited include low-quality content, poor recommendations and inappropriate messaging.

59% of managers saying that they had to invest additional time to correct or redo work created by AI. Similarly, 53% said their direct reports had to take on extra work, while 45% said they had to bring in co-workers to help fix the mistake.

they damaged client relationships.

While 18% of managers said they did not suffer any financial losses from the mistakes, and 20% said those losses were less than $1,000, a significant number reported bigger losses. Twelve percent said those losses were more than $25,000, while 11% said between $10,000 and $24,999. Another 27% placed the value of those losses above $1,000 but below $10,000.

34% said Gen Z followed by 26% who blamed millennials, with 18% citing Gen X and just 9% saying it was baby boomers.

The laws of physics and physical chemistry areinaccurate within a probable relative error of the order of I Ivn,where n is the number of molecules that co-operate to bringabout that law - to produce its validity within such regions ofspace or time (or both) that matter, for some considerations orfor some particular experiment.

false positives

Surprisingly, the American author who is quoted most in the OED isnot Mark Twain or Emily Dickinson or Edgar Allan Poe, but rather EdwardH. Knight, a patent lawyer and expert in mechanics who wrote the AmericanMechanical Dictionary and The Practical Dictionary of Mechanics. Knight isthe seventy-fourth-most cited author in the Dictionary, quoted morefrequently than Percy Bysshe Shelley, George Eliot or Ralph Waldo Emerson(who comes in at 116, the next-most quoted American).

Round half to even, which rounds to the nearest even number. With this method, 1.25 is rounded down to 1.2. If this method applies to 1.35, then it is rounded up to 1.4. This is the method preferred by many scientific disciplines, because, for example, it avoids skewing the average value of a long list of values upwards.

Input Crowd, Output Meaning

we've achieved a level of prosperity for a huge number of people that was not typical of the past i mean most countries have a big middle class as well as an extremely wealthy upper 00:50:00 class but the number of people in abject poverty in the world today living on less than two dollars a day is greater than the entire population of the world 00:50:13 only 100 years ago so that's not progress

the more you learn, the smaller your error will tend to be.

A nice and easy way to report results of an ANOVA in R is with the report() function from the {report} package:

Those times are better captured in the ten volumes, 414,825entries, and 1,827,306 quotations that were finally published in 1928.

Some Americans did write directly to Murray, and these – 196 ofthem – are the ones underlined in the address books. They represent 10 percent of all the Dictionary People with addresses and produced a total of238,080 slips that crossed the ocean before coming to rest on Murray’s deskin the Scriptorium.

Ranking below Thomas Austin, who sent in 165,061 slips, and WilliamDouglas, who sent in 151,982, there is a big drop to the third-highestcontributor Dr Thomas Nadauld Brushfield, who sent in 70,277 slips.

And yet he desperately needed the help of Subeditors because the task wastoo massive to do alone. Two years into the job, Murray had estimated thathe had sent out 817,625 blank slips to Readers. If they returned them withquotations, and if he spent a minimum of 30 seconds reading each one andallocating it to the correct sense of an entry, it would take him three workingyears to get through a third of the materials gathered.

Murray received a poignant letter in 1906 fromthe wife of William Sykes of South Devon who had been a one-timeassistant, and faithful Reader and Specialist for twenty-two years, sending in atotal of 16,048 slips: ‘My dear husband died last Friday, the day he receivedyour letter, he was able to read it, and wrote your name in one of the books Iam going to send you eight hours before he died. It took him an hour to writeit, but he made up his mind to do it, and did. The last words he ever wrotewere to you.’ A poignant last line from the impoverished widow reads, ‘I shallsend the books when the probate duty has been paid.’

From the moment in March 1879 whenMurray signed the contract with Oxford University Press to be the next Editorof the Dictionary, and he took possession of 2 tons of slips at his house, hisfamily was immediately part of the project (whether they liked it or not)sorting out the slips. Their house was a workplace and the family aworkforce.

The most prolific Reader in Europe – we might call him a ‘super-contributor’ – was Hartwig Helwich, a professor at the University of Viennawho wrote out the entire Cursor Mundi onto 46,599 slips. His efforts madethe medieval poem the second-most-frequently cited work in the Dictionaryafter the Bible (though in the current OED, it has dropped to eleventh in thetop sources).

Get it right and we will see a lot less of our precious minerals, metals and resources dumped into landfill

the suicide is up by 30% depression rates are skyrocketing 36% of 00:07:57 Americans report feeling lonely frequently 45% of teenagers say they feel despondent and hopeless most of the time the number of people who have no who say they have no close personal friends has gone up by four 00:08:10 times 36% more Americans are not in a romantic relationship uh the number of people Americans who rate themselves in the lowest happiness category has gone up by 50%

weakly informative approach to Bayesian analysis

Science is not described by thefalsification standard, as Popper recognized and argued.4 In fact, deductive falsification isimpossible in nearly every scientific context. In this section, I review two reasons for thisimpossibility.(1) Hypotheses are not models. The relations among hypotheses and different kinds ofmodels are complex. Many models correspond to the same hypothesis, and manyhypotheses correspond to a single model. This makes strict falsification impossible.(2) Measurement matters. Even when we think the data falsify a model, another ob-server will debate our methods and measures. They don’t trust the data. Sometimesthey are right.For both of these reasons, deductive falsification never works. The scientific method cannotbe reduced to a statistical procedure, and so our statistical methods should not pretend.

So where do priors come from? They are engineering assumptions, chosen to help themachine learn. The flat prior in Figure 2.5 is very common, but it is hardly ever the best prior.You’ll see later in the book that priors that gently nudge the machine usually improve infer-ence. Such priors are sometimes called regularizing or weakly informative priors.They are so useful that non-Bayesian statistical procedures have adopted a mathematicallyequivalent approach, penalized likelihood. These priors are conservative, in that theytend to guard against inferring strong associations between variables.

one assumes the population size andstructure have been constant long enough for the distribution of alleles to reach a steady state

Andrew Gelman’s

The global illicit trade in wildlife may be worth up to $20,000,000,000 annually and the value of legal wildlife trade in the United States was recently estimated at $2,800,000,000 annually.

people with poor family relationships and no close friends “are ten times more likely to suffer from significant mental health challenges” compared to those with many close family bonds and friendships.

Statistics collected in hundreds of cities in the United States show that between a third and a half of the school children fail to progress through the grades at the expected rate; that from 10 to 15 per cent are retarded two years or more; and that from 5 to 8 per cent are retarded at least three years. More than 10 per cent of the $400,000,000 annually expended in the United States for school instruction is devoted to re-teaching children what they have already been taught but have failed to learn.

How To Perform T-Tests In Microsoft Excel

An Introduction to t Tests | Definitions, Formula and Examples

are already done: E[S]E[(y−y^)2]=E[(y−y^)2]=(y−E[y^])2+E[(E[y^]−y^)2]=[Bias]2+Variance

Aleatoric music (also aleatory music or chance music; from the Latin word alea, meaning "dice") is music in which some element of the composition is left to chance, and/or some primary element of a composed work's realization is left to the determination of its performer(s). The term is most often associated with procedures in which the chance element involves a relatively limited number of possibilities.

formula

the positivist paradigm

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