The Giant Hadron Collider (LHC) close to Geneva, Switzerland grew to become well-known world wide in 2012 with the detection of the Higgs boson. The remark marked an important affirmation of the Commonplace Mannequin of particle physics, which organizes the subatomic particles into teams much like components within the periodic desk from chemistry.
The U.S. Division of Power’s (DOE) Argonne Nationwide Laboratory has made many pivotal contributions to the development and operation of the ATLAS experimental detector on the LHC and to the evaluation of indicators recorded by the detector that uncover the underlying physics of particle collisions. Argonne is now enjoying a lead position within the high-luminosity improve of the ATLAS detector for operations which might be deliberate to start in 2027. To that finish, a crew of Argonne physicists and computational scientists has devised a machine learning-based algorithm that approximates how the current detector would reply to the significantly elevated knowledge anticipated with the improve.
As the biggest physics machine ever constructed, the LHC shoots two beams of protons in reverse instructions round a 17-mile ring till they method close to the velocity of sunshine, smashes them collectively and analyzes the collision merchandise with gigantic detectors corresponding to ATLAS. The ATLAS instrument is in regards to the top of a six-story constructing and weighs roughly 7,000 tons. Immediately, the LHC continues to check the Higgs boson, in addition to deal with basic questions on how and why matter within the universe is the best way it’s.
“A lot of the analysis questions at ATLAS contain discovering a needle in a large haystack, the place scientists are solely serious about discovering one occasion occurring amongst a billion others,” mentioned Walter Hopkins, assistant physicist in Argonne’s Excessive Power Physics (HEP) division.
As a part of the LHC improve, efforts at the moment are progressing to spice up the LHC’s luminosity — the variety of proton-to-proton interactions per collision of the 2 proton beams — by an element of 5. This can produce about 10 occasions extra knowledge per yr than what’s presently acquired by the LHC experiments. How nicely the detectors reply to this elevated occasion fee nonetheless must be understood. This requires operating high-performance laptop simulations of the detectors to precisely assess recognized processes ensuing from LHC collisions. These large-scale simulations are expensive and demand massive chunks of computing time on the world’s finest and strongest supercomputers.
The Argonne crew has created a machine studying algorithm that will likely be run as a preliminary simulation earlier than any full-scale simulations. This algorithm approximates, in very quick and more cost effective methods, how the current detector would reply to the significantly elevated knowledge anticipated with the improve. It entails simulation of detector responses to a particle-collision experiment and the reconstruction of objects from the bodily processes. These reconstructed objects embrace jets or sprays of particles, in addition to particular person particles like electrons and muons.
“The invention of recent physics on the LHC and elsewhere calls for ever extra advanced strategies for large knowledge analyses,” mentioned Doug Benjamin, a computational scientist in HEP. “Nowadays that normally means use of machine studying and different synthetic intelligence strategies.”
The beforehand used evaluation strategies for preliminary simulations haven’t employed machine studying algorithms and are time consuming as a result of they contain manually updating experimental parameters when circumstances on the LHC change. Some may miss vital knowledge correlations for a given set of enter variables to an experiment. The Argonne-developed algorithm learns, in actual time whereas a coaching process is utilized, the assorted options that have to be launched by way of detailed full simulations, thereby avoiding the necessity to handcraft experimental parameters. The tactic may seize advanced interdependencies of variables that haven’t been potential earlier than.
“With our stripped-down simulation, you’ll be able to study the fundamentals at comparatively little computational price and time, then you’ll be able to way more effectively proceed with full simulations at a later date,” mentioned Hopkins. “Our machine studying algorithm additionally supplies customers with higher discriminating energy on the place to search for new or uncommon occasions in an experiment,” he added.
The crew’s algorithm might show invaluable not just for ATLAS, however for the a number of experimental detectors on the LHC, in addition to different particle physics experiments now being carried out world wide.
Supplies offered by DOE/Argonne Nationwide Laboratory. Authentic written by Joseph E. Harmon. Word: Content material could also be edited for fashion and size.


It’s almost two years since Apple introduced two Mac mini models with the M1 chip alongside an M1 MacBook Air and MacBook Pro (reviewed here: M1 Mac mini review). Of all the M1 Macs the Mac mini was our favourite as we thought it offered the best in terms of specs coupled with a low price – however, it’s now looking rather dated and our advice right now would be don’t buy because something better is coming.
Apple launched a M2 MacBook Air and M2 MacBook Pro back in June 2022, but the Mac mini was neglected in that round of updates. Mac fans are wondering when Apple will update the Mac mini with an M2 chip. In this article we will look at what the M2 Mac mini might offer, and when it will launch.
Work on a new M2 Mac mini is evidently taking place at Apple. Bloomberg’s Mark Gurmanhas reportedthat Apple is testing an M2 Mac mini, codenamed J473, with eight CPU cores and 10 GPU cores.Gurman has also indicated that Apple has been testing a Mac mini with an M1 Pro, codenamed J374 and an M1 Max Mac mini. Those Pro models still haven’t arrived and an M2 Pro Mac mini is now looking more likely – we discuss the M2 Pro Mac mini separately, so if that’s the Mac mini upgrade you are most interested in read: New Mac mini 2022 with M2 Pro: What you need to know.
M2 Mac mini release date: When will the next model arrive?
Apple introduced the M1 chip in the Mac mini, MacBook Air and 13in MacBook Pro at its One More Thing event on November 10, 2020. Two of those Macs now have M2 chips, so what about the Mac mini? The M1 Mac mini has now passed its two year anniversary and we continue to wait for an M2 Mac mini. (Read about why we say it’s not a good time to buy the Mac mini.)
In a Power On newsletter on in October 2022 Bloomberg’s Mark Gurman indicated that “Apple still has some Macs to debut over the next several months, including a new Mac mini and a MacBook Pro.” Then in a subsequent Power On newsletter on October 30, with the headline “New Macs coming next year” he wrote that “Apple’s next group of Macs probably won’t launch until early next year”. As for when: Gurman is banking on the first half of March.
As for how Apple will make that announcement, Apple issues a press release about new iPads and iPad Pro models, so it may do the same for the new Mac mini (and anticipated new MacBook Pro models). There have been suggestions (Gurman, again) that Apple’s upcoming new productsmight not be enough to justify an event.
So we could see a new Mac mini in March, or we could potentially see something sooner if Apple wants to reserve its Apple Spring Event for the launch of the VR/AR headset that has long been rumored.
The long delay in getting the M2 Mac mini isn’t entirely unexpected. Apple does have previous when it comes to neglecting the Mac mini which went from 2014 to 2018 without an update. But maybe there is something coming that warrants the delay?
Mac mini M2 design: Will the Mac mini look the same?
One explanation for the long wait for a new Mac mini could be that Apple is changing the design. The Mac mini has had a similar square design ever since Apple introduced it in 2005, over time it has gotten thinner, lost the CD drive, and had a color change. The next Mac mini could get a brand new look, according to reports.
Jon Prosser claimed back in June 2021 that the next Mac mini will shrink even further, which could put it at less than an inch tall. The body could be getting a change too. While the Mac mini has had its aluminum chassis since 2010, Apple is reportedly considering a “plexiglass” top, according to Prosser. That could mean Apple will bring a little pizzazz to the Mac mini with a new color. The Mac mini is currently limited to silver and space gray.
It should be pointed out that Prosser has been wrong on several occasions before—he’s had some big hits, but his misses include one so severe that he had to shave his eyebrows—so the information should be taken with a pinch of salt. AppleTrack currently gives him an accuracy rating of 74.2% (that score changes all the time). Read more about the Apple leakers in our round up: The Apple leakers worth following.
Ian Zelbo made some illustrations based on Prosser’s findings. The renders indicate that the new mini will come in silver and white, rather than one of the new bright colour options seen on the 24in iMac. These renders are based on information from Prosser’s sources, but they may still give a good idea of what Apple could unveil.
Render by Ian
Prosser isn’t the only one expecting a new look Mac mini. In August 2021, Mark Gurman of Bloomberg reported that the Mac mini will have “an updated design and more ports than the current model.” Furthermore, the circular rubber pad on the bottom of the new Mac mini is said to be replaced with two horizontal strips as we see on the underside of the 24-inch iMac stand.
However, there may be no redesign in the pipeline. In March 2022 a tweet from an account claiming to be for analyst Ming-Cho Kuo stated that the 2023 Mac mini will have the same form and design as the current model.
M2 Mac mini spec & features: What will be different
The one change we’re certain of with the new Mac mini is that it will get an M2 chip like the one found in the 2022 MacBook Air and 2022 13-inch MacBook Pro. Assuming it’s the same, these are the specs:
- CPU: 8-core CPU with 4 performance cores and 4 efficiency cores
- GPU: 8-core or 10-core GPU
- Up to 24GB Unified Memory
To find out more about the M2 chip read: Apple M2 Chip: Everything you need to know.
There are reports that M3 Macs could launch in 2023, in which case an M2 Mac mini needs to arrive sooner rather than later to avoid being immediately obsolete.
The current Mac mini models offer two USB-4/Thunderbolt ports, two USB-A port, an HDMI 2.0 port, as well as Ethernet snd a headphone port. It’s likely that this will carry through to the new iteration. The M1 Pro version of the Mac mini could get two additional Thunderbolt/USB-C ports for a total of four, though rumors say the new mini likely won’t bring back the SDXC card slot that appeared on the MacBook Pro and Mac Studio.
M2 Mac mini price: How much will the new Mac mini cost?
Currently the M1 Mac mini is priced as follows:
- M1 Chip with 8-Core CPU and 8-Core GPU, 256GB: $699/£699
- M1 Chip with 8-Core CPU and 8-Core GPU, 512GB: $899/£899
- 3.0GHz Intel Core i5 6-Core Processor with Intel UHD Graphics 630, 512GB: $1,099/£1,099
It is possible that Apple will do the same as it did with the MacBook Air and keep the M1 Mac mini on at $699/£699 while introducing two new M2 models to replace the $899 and $1,099 options. We hope that Apple includes an M1 Pro or M2 Pro model in the line up though.
If you aren’t based in the U.S. expect prices to increase as they have for every other product Apple has launched in 2022. We’ve seen price hikes of as much as £150 per product in the U.K., for example.
Will Mac Mini be updated in 2022? ›
Bloomberg's Mark Gurman initially said that the Mac mini could be refreshed in 2022, but he later said that Apple has decided to wait until 2023 to introduce new Macs. Apple analyst Ming-Chi Kuo has also said that Apple won't release an updated model until 2023.Is there an M2 Mac mini coming? ›
The one change we're certain of with the new Mac mini is that it will get an M2 chip like the one found in the 2022 MacBook Air and 2022 13-inch MacBook Pro. Assuming it's the same, these are the specs: CPU: 8-core CPU with 4 performance cores and 4 efficiency cores.Is it worth waiting for Apple M2 chip? ›
We still recommend that unless you're creating 3D content, making videos, writing apps, or doing other tasks that push the CPU, it's unlikely that you will notice the additional performance the 2023 MacBook Pro models will bring. But if you don't need a new Mac just yet, it sounds like you'll be rewarded for the wait.Is it worth upgrading to M2? ›
The M2 MacBook Pro is better suited to buyers who are upgrading from an older machine, rather than 2020's M1 model. Spearheaded by the M2 chip, the device's series of minor improvements offer a good overall package for these buyers, ensuring that they get a slightly more modern and capable machine.Should I upgrade Mac mini Monterey? ›
Yes! Upgrading to macOS Monterey is highly recommended, especially if you have a newer Mac powered by one of the Apple silicon chips, such as the M1 or M2 processors. Not only will you get better performance, but you'll also be able to take advantage of all the new features, some of which are really helpful.How long do Mac Minis last? ›
So, in answer to the question: How long do Macs last? We'd say five to eight years, but beware that you probably won't be able to replace any faulty parts in a Mac when more than five years has passed since Apple last sold it. Before you buy a new Mac, read our article about the best time to buy a Mac or MacBook.Is M2 better than M1 Apple? ›
In our testing, the M2 has an 18 percent faster CPU than the M1. The new chip doesn't just mean that the new Macs can accomplish more though, they use less power doing so: Apple promises an increased CPU performance of 18 percent with the same energy consumption thanks to various optimizations.What's better M2 or M1 Pro? ›
The M2 levelled the playing field in some senses by adopting the capabilities of the M1 Pro's dedicated media engine and the chip is more powerful in single-core tasks with its higher 3.49GHz CPU clock speed, but the M1 Pro is considerably better in multi-core and graphics tasks.Is Apple M2 better than Intel? ›
M2 MacBook is actually slower than a Core i7, Apple says. Expectations have been running exceptionally high for Apple's new M2 processor to outpace Intel's best CPUs, but in the end, the M2 won't beat Intel's fastest chips. That was the news from Apple's WWDC keynote Monday.Is M2 Mac worth it? ›
The MacBook Air M2 is Apple's latest swing at its fanless Air design. Like the M1 model, it's among the best laptops you can buy right now because of its excellent portability, battery life, and, above all, performance.
Is it worth upgrading to M2 from M1? ›
Not really. The M2 is basically just a slightly more improved M1 — it is an iterative upgrade. Nothing more, nothing less. If you already own an M1-based machine you shouldn't even think about upgrading as you're not going to notice any tangible difference.Is M2 chip worth the extra money? ›
The new MacBook Air M2 is here. Like the recently released MacBook Pro M2, this new notebook is the latest to feature the Apple M2 chip — making it one of the most performant and overall best laptops you can buy. Despite its steep $1,199 starting price, it's arguably worth every penny due to the value it provides.Can M2 overheat? ›
The new MacBook Air is thinner than its predecessors, and while it has received positive reception, there have been reports of users complaining that their MacBook Air is suffering from overheating and throttled performance.Is M2 faster than i9? ›
The Apple M2 takes its single-thread score of 4,130 points and creates a very small gap of +0.93% between itself and the highly praised Intel Core i9-12950HX. This result leaves the M2 as the laptop CPU champion while also holding fourth position in the desktop chart.How much faster is M2 Mac? ›
The M2 is the latest and most efficient Apple Silicon, with an 18% faster CPU, a 35% faster GPU and a 40% faster neural engine than previous generations, as well as 50% more memory bandwidth.Will Monterey slow down my Mac Mini? ›
Why is my Mac slow after Monterey update? Updating your Mac to the latest macOS Ventura build can slow down the performance as the new build is in Beta and has some bugs and other issues. In addition, apps and services are still being optimized for the new OS, so your system might be having performance issues.Can I go back if I upgrade to Monterey? ›
Before Downgrading macOS 12 Monterey
If your Mac originally shipped with macOS 12 Monterey, then that is its default version of macOS, and no earlier versions can be installed. If you've upgraded to macOS 12 Monterey from another version, then you can go back — and it's not too difficult.
To fix the issue where Monterey slows down your Mac, go back to a previous macOS version. Then, force-stop all the unnecessary programs running in the background. Additionally, delete your junk files, and uninstall the programs you no longer use to declutter your Mac. If the problem persists, reset NVRAM or PRAM.Should I turn off my Mac mini every night? ›
A good rule of thumb is that if you're only going to be away from your Mac for an hour or two or even overnight, letting it sleep is probably the best method. Any longer than that and you'll want to power it down, for reasons outlined in the next section.Should I always shut down my Mac mini? ›
Yes, it is 100% beneficial. What's more, restarting your Mac is the number one tip on any troubleshooting list. Shut Down mode can help fix many Mac issues; plus, it will also flush your RAM and allow your device to cool down, protecting it from overheating. Go to the main Apple menu > Shut Down.
How often should I turn off my Mac mini? ›
One question that new Mac users often ask is whether they should shut down their computers at the end of the day or just let them sleep. The quick answer is that sleep is better for your Mac, and in today's Tech Tip we'll show you why it's better to just let your Mac go to sleep than shut it down each day.Is the M2 chip better than i7? ›
The 12-core-based Core i7 outperforms the M2 chipset in terms of performance but falls short in terms of power efficiency. Yes, Apple admits that the 12-core Core i7 processor is faster than the M2 chipset. However, the M2 chipset retains an advantage in terms of power consumption.What's the difference between Mac M1 and M2? ›
One of the most important differences between the current and previous-generation MacBook Airs is their Apple silicon chip. The M1 chip, introduced in November 2020, is based on Apple's A14 Bionic chip. On the other hand, the M2 is based on the A15 Bionic chip from the iPhone 13.Is 256 GB of storage enough? ›
The reality is that 256GB of internal storage is probably going to be plenty enough for most people who don't already have (or anticipate having) a ton of locally stored photos, video, video games, or music that can't either be easily offloaded into the cloud, or to a backup drive.Is the M2 slower than M1? ›
Storage speed performance
Unfortunately, the MacBook Air M2's storage read and write speeds are much slower than the MacBook Air M1's same speeds. In our testing, the MacBook Air M2's SSD was up to 50% slower than the MacBook Air M1's SSD.
Most people will be absolutely fine with 8GB of unified memory on their M2 MacBook. Those who know they need more – or who plan on undertaking increasingly intensive, creative workflows or complex software development – will do well to upgrade. It's that simple.What is next after M2? ›
M3: Late 2023
It will likely be its first Mac processor to use a 3nm process and offer a significant speed boost over the M2 due to a new architecture, possibly with more GPU and CPU cores, higher RAM limits, and extra Thunderbolt ports.
In other M1 and M2 devices, 8GB of memory would not be ideal but wouldn't lead to significant slowdowns. But part of the reason other M1 and M2 Macs can get away with only 8GB of memory is because they have super fast SSDs that the processor can use for memory in a pinch.How much faster is M2 chip than Intel? ›
Apple M2 chips are 1.4x faster than the M1 and 15x faster than Intel-based MacBook Air options. They also have more memory, better security, and other great Apple next-generation custom technologies.Which chip is better M2 or M1 Max? ›
Since the number of transistors correlates to better performance, it looks like the M1 Max is more powerful than the M2, despite its new architecture. The M1 Max also packs a lot more cores, with the chip packing 10 CPU cores and 32 GPU cores, compared to the Apple M2's 8 CPU cores and 10 GPU cores.
Is the M2 processor good? ›
The M2 chip is 1.4x faster than the M1, with an 18 percent faster CPU, a 35 percent more powerful GPU, and a 40 percent faster Neural Engine. Geekbench benchmarks have confirmed that the M2 chip is up to 20 percent faster than the M1 chip when it comes to multi-core performance.How much will the M2 Mac cost? ›
M2 MacBook Air: Price
The M2 MacBook Air starts at $1,199 in the U.S. and £1,249 in the U.K. There are two models that are priced as follows: M2 Chip with 8‑Core CPU and 8‑Core GPU, 8GB RAM, 256GB SSD: $1,199/£1,249. M2 Chip with 8‑Core CPU and 10‑Core GPU, 8GB RAM, 512GB SSD: $1,499/£1,549.
For comparison, Apple said the the M2 has seen an 18% CPU performance increase compared to the M1, while the GPU has seen a 35% graphics performance boost.How much RAM does the M2 chip have? ›
The M2 offers an 8-core CPU, 10-core GPU, as well as up to 24GB RAM. That's more GPU cores than the M1, which had 8 GPU cores, and a higher RAM limit, which topped out at 16GB RAM on the M1.Can M2 chip run Windows? ›
In the past, you could install Windows on your Mac using Boot Camp, but that won't work if you've upgraded to an M1 or M2 Mac—you need an Intel processor to run the app, but Apple doesn't sell Macs with those chips anymore. However, you don't need to install Windows to run Windows-only apps at all these days.Is 8GB unified memory enough? ›
For the M1-based 16 inch MacBook Pro (and MacBook Air), 8 GB of unified memory should be plenty for a regular user, much like 16GB RAM was on the Intel-based versions and Big Sur. While applications and the operating system used to require less than virtual memory of 8GB RAM, Intel considered it acceptable.Does M2 need a fan? ›
The new Apple M2 requires an active cooling solution and is even slightly throttled in the MacBook Pro 13. The first MacBook with Apple's new M2 SoC is the familiar MacBook Pro 13. It uses a cooling solution with a fan, but it still cannot handle the full performance of the Apple M2 for more than a couple of seconds.Does M2 need heat sink? ›
Do NVMe SSDs need heatsinks? The answer is Yes. M. 2 drives are not only faster and more compact but also eliminate the need for bulky power and data cables.What is too hot for M 2 SSD? ›
“The controller and all the other components … are good up to 125 degrees Celsius (257 degrees F),” he said, “but the NAND isn't, and the SSD will go into critical shutdown if it detects that the temperature of the NAND is above 80 degrees Celsius (176 degrees F) or so.”What Intel chip is M2 comparable to? ›
Apple has compared the M2's performance to “the latest ten-core notebook processor”, which essentially means the Intel Core i7–1255U found in Samsung's Galaxy Book2 360. Both builds were also said to come with 16GB of RAM. The M2 is then said to be 1.9× faster than the aforementioned Intel processor.
Is i9 better than Apple M1? ›
Overall, it's safe to say that the new Intel Core i9-12900HK is performing better than both M1 Pro and the M1 Max in the CPU-centric benchmarks. Even in tests involving the graphics unit, the MSI is able to hold its own against the MacBook Pro.Is the M2 faster than the M1 Ultra? ›
With the M2, Apple uses four performance cores with a maximum clock speed of 3.49GHz, which, believe it or not, is faster than what you get in the 20-core M1 Ultra. That shows the power of the new high-performance cores in the M2 chip.Is M2 64 bit? ›
|Series||Apple Apple M-Series|
|GPU||Apple M2 10-Core GPU ( - 1398 MHz)|
|64 Bit||64 Bit support|
|Announcement Date||06/10/2022 = 218 days old|
Not surprisingly, the A16 Bionic in the iPhone 14 Pro is the fastest. The iPhone 14 and iPhone 13 both have an A15 Bionic processor, but the iPhone 13 has one fewer GPU core than the iPhone 13 Pro, which gives it better graphics performance.Is there a new Mac coming out in 2022? ›
Announced in June 2022, the MacBook Air is the newest Mac in Apple's lineup. The previous, M1-based MacBook Air was released in November 2020, but before that, Apple updated the MacBook Air annually.Will there be new macOS in 2022? ›
macOS Monterey was succeeded by macOS Ventura, which was released on October 24, 2022.Is there going to be a new Mac mini? ›
Apple is expected to release the updated Mac mini in early 2023. There were initially rumors that the Mac mini could get a design update, but more recent information suggests Apple will continue to use the same form factor if there is a refresh coming, with no notable design changes planned.How long do Mac Minis Get updates? ›
Operating Systems Support 4-6 years
Apple will generally upgrade the MacOS once per year. Most Macs will be able to be upgraded to the current operating system for about 4 to 6 years.
Apple's "Far Out" event was a media event held on Wednesday, September 7, 2022, where the company unveiled the iPhone 14 lineup, new Apple Watch models, and updated AirPods Pro. The iPhone 14 lineup included four new models: the iPhone 14, iPhone 14 Plus, iPhone 14 Pro, and iPhone Pro Max.What is Apple discontinuing 2022? ›
Following the introduction of the Mac Studio and Studio Display in March, Apple discontinued the 27-inch iMac, which was the final iMac model with an Intel processor. With the iMac Pro also discontinued last year, the 24-inch iMac with the M1 chip is currently the only all-in-one desktop computer sold by Apple.
Will there be a new Mac in 2023? ›
In an October report, Bloomberg's Mark Gurman wrote that the new MacBook Pro “probably won't launch until early next year ”. In a December PowerOn newsletter, Gurman reiterated that the “14-inch and 16-inch MacBook Pros are arriving early next year  with M2 Pro and M2 Max options.”What is the best Mac in 2022? ›
- Best MacBook Overall: Apple MacBook Air With M2 Processor.
- Best MacBook On A Budget: Apple MacBook Air With M1 Processor.
- Best MacBook For Creatives: Apple MacBook Pro 16-Inch.
- Best MacBook Pro For Portability: Apple MacBook Pro 13-Inch.
Introduced at WWDC 2022, macOS Ventura is the current version of macOS, the operating system that runs on the Mac. macOS Ventura is a significant update that introduces a new multitasking experience, overhauls multiple apps, and adds updated capabilities across the platform.What is the best Mac mini to upgrade? ›
The 2012 Mac Mini held a special place in many hearts thanks to how easy it was to upgrade. It remains Apple's last properly upgradeable Mac Mini, and even in 2020 can be a pretty handy little computer with the right bits inside. Here we'll walk you through breathing new life into your old Mac for not a lot of money.What is a Mac mini good for? ›
It's the most affordable Mac that Apple sells. It has built-in in apps such as FaceTime, iBooks, Maps, iTunes and Mail. There are multiple ports, making it more like a PC than a media device.Is the Mac mini the best value? ›
Is the Mac mini Worth it? The Mac mini is an excellent computer for many people looking to enter the Mac world at the lowest price possible. It offers a solid selection of ports, Apple silicon, and the smallest size for a Mac desktop.Why is my Mac update taking so long Monterey? ›
Various reasons lead to the Mac update taking so long in Monterey. For starters, if the internet connection is slower or you are downloading the macOS at peak hours, it might take more time. If upgrading from a much older version, expect a longer time to download the software.How many GB do I need to update my Mac? ›
How much space does macOS Catalina need on your Mac? macOS 10.15 Catalina requires 4.9GB of free space to download. You need 12.5GB when upgrading from OS X El Capitan or later, or 18.5GB from OS X Yosemite or earlier.How long should a Mac update take? ›
You may want to leave your Mac downloading the operating system update over night. And that's before you even start the installation at which point you won't even be able to use your Mac. Expect this to take up to an hour. Ventura is about 6.37GB, which can take a long time to download on a slow network.


With the arrival of Industry 4.0 and the Industrial Internet of Things (IIoT), a digital transformation is currently underway. Manufacturers have used data analysis to improve efficiency and advance their market share for many years. But the most significant change today is how data is collected. Many companies still use fragmented, traditional methods for data capture, with staff manually checking and recording factors, filling forms, and writing down operation and maintenance histories for the machines on the floor. Unfortunately, these methods are highly inaccurate due to human errors. They are also time-consuming, open to bias, and do not generate the quality of analysis required for accurate decision-making.
Equipment connected through sensors and edge devices feeds massive volumes of data to cloud-based analytics platforms that can analyze and understand data faster than human perception. This data can then be used to drive real-time decision-making and significant process improvement throughout the company. With accurate and real-time data, manufacturers can make better, faster decisions
Automated machine data collection is driving the next generation of manufacturing analytics, unlocking a myriad of advanced analytics use cases in manufacturing that range from simple monitoring and diagnosis to predictive maintenance and process automation.
In manufacturing analytics, data capture that records events can be leveraged to increase equipment utilization, reduce cost, drive process improvement, reduce human-based errors, and do so at a depth that reveals accurate machine conditions and trends in production.
By applying analytics, real-time data can be leveraged to do more than prevent breakdowns. It can predict with high accuracy the likelihood of a breakdown and the moment it will occur. This reduces costs by allowing technicians to perform repairs at the machine’s optimal time and stage parts. This reduces overall downtime and increases productivity.
Demand planning can be complex. With the addition of data science methods, end-to-end control of the supply chain can be used in conjunction with real-time shop floor data to better manage purchasing, inventory control, and transportation. Highly accurate demand plans can be generated that identify trends that would otherwise go unnoticed.
With a better understanding of how long it takes to make parts, how long job runs will take, and the expected costs and profit of a given job, manufacturers can better estimate their need for material to improve planning.
Cycle times play a major role in pricing. And knowing precise times for part creation and the associated costs allows for accurate cost models and optimized pricing strategies. Setting them too low reduces profitability while setting them too high may impact demand. An advanced analytics platform for manufacturing can bring this data forward to ensure prices are set appropriately. MachineMetrics can help manufacturers optimize their job standards to ensure accurate cycle times.
For many manufacturers, warranty support can be a drain. Often, warranties consist of a “one-size-fits-all” approach that’s more general. This allows uncertainty and unexpected problems into the equation.
By applying data science in manufacturing and capturing information from active warranties in the field, products can be improved or changed to reduce failure and therefore cost. It can also lead to more informed iterations for new lines of products to proactively avoid field complaints.
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01 / Artificial Intelligence
02 / Machine Learning
03 / Deep Learning
Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.
As well as having interest in the latest technological trends we believe in creating a technological solution to an everyday problem, whether at home, offices or education institutions, that will stand out.
Providing startups and companies analytical and product solutions with state of the art technologies in data analytics, AI/ML.
We have a background in Data Analytics and Machine Learning, and firmly believe that AI/ML is the need of the hour. We can help you by providing you with the technical solution for your problems.


Table of Contents
- Introducing NLC SimNet: A Comprehensive Simulation Tool for Autonomous Vehicle Development
- Exploring the Benefits of NLC SimNet for Autonomous Vehicle Design and Testing
- How NLC SimNet Helps Automakers Keep Up with Autonomous Vehicle Technology Trends
- Designing Autonomous Vehicle Experiences with NLC SimNet
- Leveraging NLC SimNet for Autonomous Vehicle Safety and Performance Verification
NLC Simnet is an innovative and powerful technology platform developed by NLC Solutions that enables organizations to develop and deploy Artificial Intelligence (AI) and Machine Learning (ML) applications quickly and easily. It provides an interactive, user-friendly environment for developers to create and test AI and ML models and applications without the need for any complex coding or programming. With NLC Simnet, organizations can quickly and easily build, deploy, and manage AI and ML applications that are tailored to their specific needs. From predictive analytics to natural language processing, NLC Simnet provides an easy-to-use platform that allows developers to rapidly create and deploy AI and ML applications.
Introducing NLC SimNet: A Comprehensive Simulation Tool for Autonomous Vehicle Development
Autonomous vehicle (AV) development has become increasingly important for the automotive industry as it looks towards a future of increased safety, efficiency, and convenience. To aid in the development of these vehicles, numerous simulation tools have been developed to provide a virtual environment for testing and evaluation. One such tool is NLC SimNet, a comprehensive simulation platform for the development and evaluation of autonomous vehicles.
NLC SimNet is a powerful simulation platform that has been designed to provide developers with a comprehensive environment for testing, validating, and optimizing AVs. It is capable of simulating a wide variety of scenarios, allowing developers to evaluate the performance of their vehicles in a virtual environment. Additionally, NLC SimNet offers a wide range of features, including realistic terrain and weather conditions, multiple vehicle types, and customizable scenarios.
The platform also includes a variety of tools that allow developers to analyze and visualize the performance of their vehicles. These include a trajectory analyzer, a camera simulator, and a performance monitor. The trajectory analyzer enables developers to track the behavior of their vehicles in various scenarios, while the camera simulator allows them to visualize the environment from the perspective of the vehicle. The performance monitor also allows developers to monitor performance metrics such as speed, acceleration, and steering angle.
NLC SimNet is an effective tool for the development and testing of autonomous vehicles. It provides developers with a comprehensive environment for evaluation and optimization, and its wide range of features make it an ideal platform for AV development. With NLC SimNet, developers can ensure their vehicles are performing optimally in a virtual environment prior to taking them out on the road.
Exploring the Benefits of NLC SimNet for Autonomous Vehicle Design and Testing
Autonomous vehicles are quickly becoming a reality, with many automotive manufacturers investing heavily in the development of self-driving cars. While these vehicles promise to revolutionize the transportation industry, their development and testing pose unique challenges. Fortunately, advancements in computing technology have enabled the creation of simulation platforms that can be used to design and test autonomous vehicles. One such platform is the NLC SimNet, an artificial intelligence-driven simulation platform that provides an accurate virtual representation of the real world. This platform can be used to design, test, and evaluate autonomous vehicles, allowing developers to identify and address problems before they arise in actual road tests.
The NLC SimNet is designed to accurately replicate the environment and conditions of actual roads and highways. It uses artificial intelligence and machine learning to generate realistic simulations of pedestrian, vehicle, and environmental behavior, which can be used to ensure the safety of autonomous vehicles. The platform also includes a variety of tools and features that allow developers to customize their simulations, such as variable weather conditions, traffic patterns, and road conditions. This allows developers to create scenarios that closely match the actual conditions that their vehicles are likely to encounter in the real world.
The NLC SimNet also provides developers with the ability to collect and analyze data from their simulations. This data can be used to assess the performance of their autonomous vehicle designs, identify potential safety risks, and optimize the design of their vehicles. This data can also be used to simulate the behavior of other vehicles on the road, allowing developers to assess the potential interactions between their vehicles and other road users.
Finally, the NLC SimNet can also be used to test autonomous vehicle designs in a variety of conditions, from urban to rural settings. This allows developers to identify potential issues before their vehicles are tested in actual road conditions, reducing the risk of accidents or other issues arising and ensuring the safety of their vehicles.
In summary, the NLC SimNet offers a powerful, realistic simulation platform for the design and testing of autonomous vehicles. By providing developers with the ability to replicate real-world conditions and analyze data from their simulations, the NLC SimNet can help developers ensure the safety and performance of their vehicles before they are tested in the real world.
How NLC SimNet Helps Automakers Keep Up with Autonomous Vehicle Technology Trends
As the automotive industry continues to evolve and adapt to the technological advancements of autonomous vehicle technology, automakers must stay abreast of the latest trends. One way for automakers to stay ahead of the curve is by taking advantage of the NLC SimNet, an advanced simulation platform for autonomous vehicle development.
NLC SimNet enables automakers to evaluate the performance of their autonomous vehicles in a realistic environment, allowing them to understand how their vehicles will respond to different scenarios. The platform is designed to simulate more than a hundred thousand kilometers of real-world driving and it features a variety of real-world roads, traffic, and challenges. This allows automakers to test their vehicles in a realistic environment to ensure their vehicles are equipped to handle the changing conditions of the road.
In addition, NLC SimNet can also be used to test and evaluate different autonomous driving technologies such as sensor fusion, adaptive cruise control, and object avoidance. This allows automakers to understand the performance of their vehicles in different driving scenarios, enabling them to make the necessary adjustments to ensure their vehicles are capable of handling the changing conditions of the road.
Finally, the platform also provides automakers with detailed analytics, allowing them to identify areas for improvement. This allows automakers to keep up with the latest trends in autonomous vehicle technology and make the necessary changes to their vehicles to ensure they are compatible with the latest autonomous driving technologies.
By taking advantage of NLC SimNet, automakers can stay on top of the latest trends in autonomous vehicle technology and ensure their vehicles are capable of handling the changing conditions of the road. This helps automakers remain competitive and ensures their vehicles are prepared for the future.
Designing Autonomous Vehicle Experiences with NLC SimNet
The development of autonomous vehicles has revolutionized the way we think about transportation. Autonomous vehicles have the potential to revolutionize the way people interact with their environment, providing a new level of mobility and convenience to drivers.
However, designing and implementing autonomous vehicles is a complex process, and requires careful consideration of many factors. To help facilitate the development of autonomous vehicles, NLC SimNet has developed a suite of tools and services to simulate real-world conditions and provide designers with the necessary data to create successful vehicle experiences.
NLC SimNet’s Autonomous Vehicle Design Suite is a comprehensive set of tools and services that allow designers to create a simulated autonomous vehicle experience. With NLC SimNet, designers can create a realistic environment in which to test their autonomous vehicle designs. The environment includes simulated roads, vehicles, pedestrians, and other elements needed to create a realistic driving experience.
Using NLC SimNet, designers can evaluate their autonomous vehicles in a variety of scenarios, such as highway driving, city driving, and parking. The simulation environment provides detailed data on the performance of the vehicle, allowing designers to adjust their designs to ensure the vehicle is performing optimally.
In addition to testing the performance of their vehicles, designers can also evaluate the user experience of their autonomous vehicles. NLC SimNet allows designers to customize the user experience by changing factors such as lighting, sound, and other aspects of the driving experience. This allows designers to create an immersive and enjoyable autonomous driving experience for their users.
NLC SimNet’s Autonomous Vehicle Design Suite is an invaluable tool for designers looking to create successful autonomous vehicle experiences. With its comprehensive set of tools and services, NLC SimNet provides the necessary data and resources for designers to create a realistic and enjoyable autonomous driving experience.
Leveraging NLC SimNet for Autonomous Vehicle Safety and Performance Verification
Autonomous vehicles (AVs) are becoming increasingly prevalent on the roads, and it is essential to ensure the safety and performance of these vehicles. NLC SimNet is an innovative new tool that can be used to verify the safety and performance of AVs. This tool uses a combination of simulation, artificial intelligence, and machine learning to create a realistic virtual environment for testing and validating the behavior of AVs.
NLC SimNet provides a comprehensive platform for testing and evaluating the performance of AVs. The platform is designed to simulate real-world scenarios and conditions that AVs would encounter on the roads. This simulation environment allows engineers to test a wide range of scenarios, such as lane changes, speed changes, and intersection navigation, and to evaluate the performance of the AVs in these scenarios. With this tool, engineers can gain insight into how the AV will respond to different environmental conditions and how the AV will interact with other vehicles.
The platform is also equipped with a variety of AI algorithms that can be used to analyze the behavior of the AVs. These algorithms are able to detect anomalies in the AVs’ behavior and provide insights into potential safety issues. The AI algorithms are also able to simulate the behavior of other vehicles in the environment, allowing engineers to gain a better understanding of how the AV will interact with other vehicles on the road.
Finally, NLC SimNet provides a comprehensive platform for validating the safety and performance of AVs. By running tests in the simulated environment, engineers can gain confidence that the AVs are performing as expected and that they are safe for use on the roads. The platform also allows engineers to gather data from the tests, allowing them to further refine the performance of the AVs.
Overall, NLC SimNet is an invaluable tool for verifying the safety and performance of AVs. This comprehensive platform allows engineers to test a wide range of scenarios and to validate the behavior of the AVs in those scenarios. The AI algorithms provide insights into potential safety issues, and the data gathered from the tests can be used to further refine the performance of the AVs. By leveraging NLC SimNet, engineers can ensure the safety and performance of AVs and make sure they are ready for the roads.
The NLC Simnet is an incredibly powerful tool for businesses looking to leverage the power of natural language processing. It provides a comprehensive suite of tools that can be used for a variety of purposes, from natural language generation to natural language understanding. The ability to customize and build on the Simnet platform makes it an ideal choice for businesses looking to quickly develop and implement natural language processing solutions. With the help of NLC Simnet, businesses can quickly and efficiently create powerful natural language processing applications that will help them better understand their customers and increase their efficiency.


Testing an operating system for a modern smart phone is nothing short of a monumental task. Testers working on a phone platform are challenged with ensuring the critical functionality customers expect from a smart phone and the 1st party applications that expose rich integrated features. Also, the platform must enable and support 3rd party applications produced by many developers around the world desired by our many customers. This is an inside look at how 4000 engineers at Microsoft work together to design, develop and test the Windows Phone platform. We will discuss the product lifecycle, and explore the continuous integration development approach used in Windows Phone Division. Of course, we will primarily focus the discussion on testing strategies including high volume automation, “health” monitoring and assessments, self-host testing, and scenario-based testing approaches used to help deliver a unique smart phone experience designed to delight our customers.
There are many challenges in testing today’s complex software systems. One of the most challenging problems is testing various combinations of multiple inputs that determine the expected output condition or state. Imagine you are testing a feature with 20 interdependent parameters and each parameter has 5 possible input conditions. The total number of possible combinations is greater than a half trillion; at one test per millisecond it would take more than 3000 years to test all possible combinations. Which combinations do we test?
Combinatorial testing is a systematic way to effectively to produce a set of tests to evaluate input combinations. Coupled with data-driven automated tests combinatorial testing can significantly increase test coverage and help reduce overall risk.
This interactive hands-on workshop provides detailed examples of how to use a powerful, highly configurable combinatorial analysis tool that is freely available to systematically test complex interdependent parameters. Attendees will learn:
- How to analyze and model variables for input parameters or configuration settings.
- How to customize a model file for pairwise analysis.
- How to use advanced features of the PICT tool such as weighting, conditionally constraining interaction of specific variables, negative testing, seeding specific combinations, and output randomization.
- How to design an automated data-driven test to increase test coverage.


The Halushka laboratory is engaged in a number of exciting scientific endeavors. These are current lab projects:
- Contamination of the GTEx Dataset. We have recently demonstrated, using the GTEx dataset, that samples prepped and sequenced at the same time contaminate each other in small, but meaningful ways. Read more here.
- Proteogenomic analysis of skeletal muscle. We have evaluated single skeletal muscle myocytes for their expression patterns based on gene and protein information.
- Tissue expression deconvolution. We have been collaborating with Matthew McCall to investigate GTEx tissues to understand tissue expression composition and how to deconvolute tissue signal into its intrinsic cell parts. This work resulted in the recent AJHG manuscript entitled "Complex Sources of Variation in Tissue Expression Data: Analysis of the GTEx Lung Transcriptome".
- xMD-miRNA-seq and the microRNAome. We have begun classifying the miRNA expression pattern of every human cell type. This work began in cultured cells resulting in a publication in Genome Research and a useful track on the UCSC Genome Browser. We are now exploring obtaining near in vivo miRNA cell expression data using a tool we developed called xMD-miRNA-seq.


Arbitrum is a layer 2 scaling solution for Ethereum that aims to improve the scalability, speed, and efficiency of the Ethereum network. It is a decentralized and permissionless platform that allows developers to create decentralized applications (dApps) that run on the Ethereum network but with much lower fees and faster transaction times.
Arbitrum achieves this by using an off-chain approach, where transactions are validated and processed by a network of validators before being submitted to the Ethereum network. This significantly reduces the congestion on the Ethereum network and allows for faster and cheaper transactions.
Arbitrum uses a technology called Optimistic Rollups, which allows for the aggregation of multiple transactions into a single batch, reducing the overall transaction fees. It also supports the development of smart contracts, which are programmable contracts that can execute automatically when certain conditions are met.
In summary, Arbitrum is a scaling solution that improves the performance of the Ethereum network, enabling faster, cheaper, and more efficient transactions while maintaining the security and decentralization of the Ethereum blockchain.
Why should an average Joe move to Arbitrum?
For an average user who frequently uses Ethereum and decentralized applications (dApps) on the Ethereum network, moving to Arbitrum can provide several benefits, including:
- Lower Fees: The high fees associated with using Ethereum have been a major pain point for many users. With Arbitrum, users can benefit from significantly lower fees, as the platform uses a scaling solution that enables faster, cheaper transactions.
- Faster Transactions: Transactions on Ethereum can often take several minutes or even hours to confirm, which can be frustrating for users. Arbitrum’s layer 2 solution uses Optimistic Rollups to process transactions off-chain and then batch them onto the Ethereum network, resulting in faster transaction times.
- Increased Capacity: With Ethereum’s current transaction processing capacity, the network can become congested during periods of high demand, leading to slower transaction times and higher fees. Arbitrum’s layer 2 solution increases the capacity of the Ethereum network, allowing more transactions to be processed at a faster speed.
- Improved User Experience: By reducing the cost and wait times associated with Ethereum transactions, Arbitrum can provide a better user experience for those who frequently use dApps on the Ethereum network.
In summary, moving to Arbitrum can provide users with lower fees, faster transactions, increased capacity, and an improved user experience. However, it’s important to note that not all dApps have migrated to Arbitrum yet, so users may still need to use the Ethereum network for some applications.
Why choose Arbitrum instead of other similar layers 2 solutions?
There are several reasons why someone might choose Arbitrum over other layer 2 scaling solutions for Ethereum:
- High Throughput and Low Latency: Arbitrum is designed to support high throughput and low latency, which means that it can handle a large number of transactions while maintaining fast confirmation times.
- Ethereum Compatibility: Arbitrum is fully compatible with Ethereum, which means that it can support all of the existing Ethereum smart contracts and dApps without requiring any changes. This is important because it allows developers to easily migrate their existing applications to Arbitrum without modifying their code.
- Decentralization: Arbitrum is built to be decentralized, which means that it is not controlled by any single entity. This is important because it ensures that the network is secure, transparent, and resistant to censorship.
- Optimistic Rollups Technology: Arbitrum uses a technology called Optimistic Rollups, which is a well-established and well-tested scaling solution. Optimistic Rollups are known for their efficiency, security, and ability to support complex smart contracts.
- Community and Adoption: Arbitrum has a growing community of developers and users who are actively building and using dApps on the platform. This means that users can access a growing ecosystem of applications, making Arbitrum a more attractive option for those looking for a wide range of dApps to choose from.
In summary, Arbitrum’s high throughput and low latency, Ethereum compatibility, decentralization, Optimistic Rollups technology, and growing community and adoption make it a compelling option for those who are looking for a reliable and efficient layer 2 scaling solution for Ethereum.
Arbitrum One and Arbitrum Nova
Arbitrum One and Arbitrum Nova are two different versions of the Arbitrum platform, both designed to improve the scalability and efficiency of the Ethereum network. Here’s a brief explanation of each:
- Arbitrum One:
Arbitrum One is the mainnet version of the Arbitrum platform. It is a fully functioning and production-ready layer 2 scaling solution for Ethereum. It uses Optimistic Rollups to process transactions off-chain and then batch them onto the Ethereum network, which results in faster transaction times and lower fees. It is fully compatible with the Ethereum network, which means that it can support all of the existing Ethereum smart contracts and dApps without requiring any changes.
Arbitrum One is designed to be highly scalable and efficient, with the ability to handle up to 4,500 transactions per second, which is significantly higher than the current capacity of the Ethereum network. It is also highly secure, using a decentralized network of validators to ensure that transactions are processed correctly.
- Arbitrum Nova:
Arbitrum Nova is a testnet version of the Arbitrum platform, which is designed to allow developers to test and experiment with the platform before deploying their applications on the mainnet. It uses the same Optimistic Rollup technology as Arbitrum One but is not connected to the Ethereum network.
Arbitrum Nova is designed to be highly flexible and customizable, with the ability to support a wide range of use cases and smart contracts. It is also highly developer-friendly, with various tools and resources that make it easy for developers to build and test their applications on the platform.
In summary, Arbitrum One is the production-ready version of the Arbitrum platform that is fully compatible with the Ethereum network and is designed for high scalability and efficiency, while Arbitrum Nova is the testnet version of the platform that allows developers to test and experiment with the platform before deploying their applications on the mainnet.
- Centralization: Some critics have raised concerns about the centralization of the Arbitrum platform since it relies on a set of validators to process transactions off-chain. While Offchain Labs has taken steps to decentralize the validator set and make it more secure, there are still concerns about the potential for centralization in the long run.
- Security: As with any blockchain technology, there are potential security risks associated with the Arbitrum platform. While the platform is designed to be highly secure, there is always the potential for vulnerabilities or attacks that could compromise the platform’s integrity.
- Reliance on Ethereum: The Arbitrum platform is currently designed to work with the Ethereum network, which means that it is subject to the limitations and risks associated with the Ethereum ecosystem. Suppose there are major changes or issues with the Ethereum network. In that case, it could potentially impact the functionality and stability of the Arbitrum platform.
In conclusion, Arbitrum is a Layer 2 scaling solution for the Ethereum network developed by the blockchain company Offchain Labs. The platform is designed to enable faster, cheaper, and more scalable transactions on the Ethereum network through the use of Optimistic Rollup technology. Here are some key takeaways on Arbitrum:
- Arbitrum is designed to address some of the major scalability challenges facing the Ethereum network by processing transactions off-chain and then batching them onto the main Ethereum network. This enables faster and more efficient processing of transactions at a lower cost.
- The Arbitrum platform is fully compatible with the Ethereum network, which means that it can support all of the existing Ethereum smart contracts and dApps without requiring any changes. This makes it easy for developers to integrate their applications with the platform and take advantage of its scalability and efficiency benefits.
- While there are potential concerns around centralization, security, and reliance on the Ethereum network that should be considered, Arbitrum has gained significant attention and adoption from the blockchain community. It has become one of the most popular Layer 2 scaling solutions for the Ethereum network and is considered a promising solution for improving the efficiency and scalability of decentralized applications on the Ethereum network.
Overall, Arbitrum has the potential to significantly improve the usability and adoption of blockchain technology by reducing transaction fees and improving the speed and scalability of decentralized applications. As with any emerging technology, however, there are risks and uncertainties associated with the use of the platform, and users and developers need to understand and evaluate these risks before using or developing applications on the Arbitrum platform.


Hmm ... faldt lige over en sandsynlig forklaring på hvorfor DV virker anderledes og 'mørkere' fra HDMI kilder.
"Dolby Vision definitely too dull on Sony OLED"
https://www.reddit.com/r/appletv/commen ... h=3b53186d
"What Picture Mode is your Sony using for the formats you're comparing? Sony's 2018 Dolby Vision implementation is a little funky…as others have mentioned, it uses Dolby Vision Low Latency for HDMI sources, which means that more of the Dolby Vision processing happens on the source device. It also limits Dolby Vision to one Picture Mode (I believe it's something along the lines of "Cinema Dark") and doesn't let you adjust anything.
HDR10, on the other hand, has multiple Picture Modes, some of which can (and will) be brighter (or less "dull") than the Dolby Vision mode. They're certainly punchier, but might not necessarily be more accurate.
So you have two options:
Let the Apple TV send the Sony Dolby Vision, and trust that the dynamic metadata and color calibration make for a more accurate (but potentially less punchy) picture. Or:
Set the Apple TV to output HDR10 instead. Apple TV will internally convert Dolby Vision source to HDR10…you'll preserve the high dynamic range and high-bit color, but you'll lose the benefit of dynamic metadata (which can change tone mapping from scene to scene, fully utilizing your panel's range).
With a regular Dolby Vision implementation (like LG's), I would recommend #1 without reservation…but the way Dolby Vision Low Latency works, there's an asterisk: Dolby Vision's greatest advantage is that it can dynamically map content to the capabilities of a given panel, and that's limited when some of the processing has to happen on the source device (if the TV is doing the processing itself, it has inside knowledge about its own capabilities that HDMI devices aren't privy to). This is why Dolby Vision looks different on the A8F's internal apps, and why the Apple TV can't do anything to fix it. Ironically, this is also the limitation of Apple TV's Dolby Vision-to-HDR10 conversion…it just doesn't have access to the same information that the TV has, and needs to make assumptions.
TL;DR: The Sony A8F's support for Dolby Vision from HDMI devices is limited, and rendered to a single Picture Mode. If that mode fits your environment, Dolby Vision is still better overall. If you need to overcome a bright room or similar, you'll have to run in HDR10 and adjust the TV as necessary…but you'll lose what remains of Dolby Vision dynamic metadata.


“Teaching is the art of lighting a spark, not the filling of a vessel.”
The Classroom of the Future: How AI is Changing Education
The education industry is going through a major shift. The way we teach and learn is increasingly influenced by artificial intelligence (AI). The way we interact with knowledge is being transformed by innovations like automated grading and virtual reality simulations. However, what does this mean for students and teachers?
Continue reading to learn more about the exciting opportunities and potential drawbacks that AI presents for education.
What is artificial intelligence (AI)? AI is a broad term that includes any technology that can carry out tasks that typically call for human intelligence. Automating processes and making decisions based on data are two applications of AI. Natural language processing and facial recognition systems are just two examples of AI applications. Additionally, AI can be utilized in education to provide students with individualized learning opportunities.
For instance, AI-powered chatbots can be used to direct students through a lesson and offer individualized advice and feedback. Additionally, AI can be utilized to analyze student data and offer suggestions for enhancing students' learning experiences. How AI is Changing the Classroom AI is changing how students learn and teachers teach.
Teachers are able to create lessons that are more engaging and interactive thanks to AI-powered technologies, and students are also getting the tools they need to succeed.
The following are some examples of how AI is affecting the classroom: Grading by computers: AI can grade written assignments, allowing teachers to concentrate on more pressing matters. Students can also receive in-depth feedback on their work from AI.
The Real World:
Students can benefit from creating immersive learning experiences through the use of AI-powered virtual reality simulations. Students can also be prepared for real-world situations through these simulations.
AI can be used to create customized learning plans for students that are tailored to their specific requirements. Analyses of Data: Analyzing student assessment data with AI can shed light on student performance and highlight areas for growth.
Benefits and Obstacles
AI has the potential to transform education, but it also has some potential obstacles. Implementing AI technologies can be costly, and maintaining them can take a lot of time and effort. The ethical implications of using AI in the classroom are also a topic of discussion. In spite of these potential difficulties, AI has the potential to offer numerous advantages to educators and students.
AI has the potential to assist educators in developing lessons that are more engaging and interactive while also offering students individualized learning opportunities.
Teachers can tailor instruction to each student's specific needs thanks to AI's ability to identify areas of growth. In conclusion, AI is increasingly influencing our teaching and learning practices.
AI has the potential to revolutionize the classroom by providing students with the individualized instruction they need to succeed and giving teachers the tools they need to create lessons that are more engaging and interactive.
Even though the ethical implications of AI in the classroom are still up for debate, the potential benefits far outweigh the potential risks.
Teachers and students alike will need to be ready to accept the changes AI brings as it develops. Man-made intelligence can possibly reform schooling in manners we can hardly comprehend, and educators and understudies should be prepared to make the most of the open doors it presents.
Here are 20 ways that teachers and students can use AI in the classroom:
Personalized Learning: AI can analyze student data to create customized learning paths for individual students.
Automated Grading: AI can grade assignments, quizzes, and tests, saving teachers time.
Virtual Teaching Assistants: AI-powered chatbots can provide instant answers to common student questions.
Data Analysis: Teachers can use AI to analyze student performance data and identify areas for improvement.
Content Recommendation: AI can suggest relevant educational resources and materials based on student progress.
Language Translation: AI can help teachers communicate with non-native English speakers by providing real-time translation.
Lesson Planning: AI can generate lesson plans and curriculum recommendations based on educational standards.
Behavioral Analysis: AI can detect signs of student disengagement or behavioral issues and alert teachers.
Parent Communication: AI-powered platforms can facilitate communication between teachers and parents.
Professional Development: AI can recommend professional development opportunities based on a teacher's needs.
11. Adaptive Learning: AI can adapt the difficulty of learning materials to match a student's skill level.
Homework Help: AI-powered tutoring apps can assist students with homework and assignments.
Language Learning: AI can provide language learners with pronunciation feedback and language practice.
Study Aids: AI-powered flashcard apps and study planners can enhance student study sessions.
Writing Assistance: AI tools can help students with grammar and style suggestions in their writing.
Virtual Labs: AI simulations can replace or supplement traditional science experiments.
Mental Health Support: AI chatbots can provide students with resources and support for mental health concerns.
Time Management: AI apps can help students schedule study time and stay organized.
Career Guidance: AI can offer career advice and recommend relevant courses or majors.
Accessibility: AI can provide accommodations for students with disabilities, such as text-to-speech or speech-to-text tools.
These are just a few examples of how AI can be beneficial in the classroom for both teachers and students. The use of AI technology can help enhance the educational experience and make learning more efficient and accessible.


The world as we know it is ever-changing. With the constant pursuit of advancement in all aspects, humans have placed great care and knowledge in pursuing ways to make our lives even easier than it already is. We have used machines to guide us in our travels, jobs, education, etc. Brilliant scientists have figured out ways for machines to aid us in our daily lives. They take complex science, tinker with them for a bit and fashion them in a way that would be simple enough to be employed in our daily lives.
Inertial Navigation Systems
One such example is the use of Inertial Navigation Systems. Simulation and systems aid in the position and orientation tracking of an object. This self-contained navigation technique has measurements which are provided by accelerometers and gyroscopes. All this positioning and tracking is based relatively on a single starting point, orientation and velocity.
Angular velocity and linear acceleration of the object being tracked and positioned is measured in Inertial Measurement Units (IMUs). This is done with the use of three orthogonal rate-gyroscopes and three orthogonal accelerometers, respectively. Once signals are released from these devices, it is now possible to track the position and orientation of a device.
INS in Daily Lives
Now that INS is thoroughly understood, how is this particular technology employed in the daily lives of the users? As previously mentioned, the world is ever-changing, ever in motion; everything moves. INS is used to track and position things. Since we are all moving, tracking and positioning objects in our daily lives is exceedingly useful.
Although not exceedingly popular yet, drive-less cars are being used today. These cars are equipped with INS technology that will provide real-time roll, pitch and heading. Not only that, but it also provides a filtered GNSS position that will maintain a robust position in case of GNSS outages. In cars like these, there is also a sensor used to precisely synchronize and stabilize equipment such as LiDAR or a camera. Autonomous navigation is not only on drive-less cars. There are also industrial vehicles, USV, ROV, AUV, UAV and UGVs that also employ INS for autonomous navigation.
Getting to explore and survey various locations is also made possible with the use of INS. Mobile mapping, indoor mapping, UAV-based survey and even hydrography is readily doable. It has even gone mobile with new solutions that require inertial navigation systems that would deliver a precise point cloud. These mobile mapping and surveying applications apply to land, air, and even marine environments. These will then show you the lay of the land, or water, as needed.
For precise dynamics and motion testing, INS can also be used. Motion is monitored and analyzed if a machine is a land, a marine or an aerial one. The gyroscopes are with very low noise, low latency and they are equipped with extended calibration to ensure that analysis on the motion of the object is sensitive. This is used in various ways such as in Heli-deck motion monitoring, car testing, and even in ship racing and monitoring.
Pointing and Stabilization
INS can also be used in gimbal cameras and antenna tracking. Inertial sensors provide latency between physical motion and output. This would then provide the best synchronization and timing to achieve the best accuracy in-camera stabilization and antenna tracking.
Life is made simpler with technology such as Inertial Navigation Systems. Upon purchasing these products, one only has to ensure maintenance and training so that they will serve their purpose accordingly. Monitoring the position, location and orientation of an object has never been this easy.


With the Augmented Reality Technology developed by IPTEK, you no longer have to touch the screen to activate and control it! In fact, the latest touch-free technology empowers users with a whole new level of onscreen interaction functionality – exciting news for business owners and customers a like!
Augmented Reality Services
AR developers build amazing AR apps that extend real-world scenes with video transmission, image recognition, and 3D rendering, providing an interactive user experience.
AR developers craft immersive brand experiences, allowing customers to interact with products, services, or spaces from their own devices.
Not just an advanced game engine, Unity can power augmented reality apps too. Toptal’s Unity developers can generate cutting-edge graphics for your applications.
ARKit is Apple’s AR SDK and works with iOS 11. Likely to become the leading choice for Apple products, ARKit supports Visual Inertial Odometry, plane detection, lighting estimation, and can be used in combination with the Unity and Unreal engines.
Specifically focused on developing AR applications for Android, Toptal’s AR developers support both location and marker-based apps through DroidAR, a freely available SDK supported on GitHub.
One of the oldest and best-established AR SDKs, Vuforia offers an impressive set of functions for developers including 2D/3D object recognition, text recognition, video playback, and virtual buttons.
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And in the case of fully autonomous vehicles, advanced systems can completely control the car or truck, and make all the navigational decisions. AI is a set of algorithms and methods instead of a single monolithic service. We believe Artificial Intelligence solutions should be strongly ai development services targeted over specific applications. All these platforms allow us to develop AI services that are customized, integrated and combined according to the requirements of individual products. Simform provides AI software development to apply smarter solutions to business problems.
No-code and low-code solutions aim to overcome this by offering simple interfaces that can be used, in theory, to construct increasingly complex AI systems. Technologies such as natural language processing and language modeling mean that soon it may be possible to use nothing more than our voice or written instructions. All of this will play a key role in the ongoing “democratization” of AI and data technology.
Use cases of AI and ML in software development
We make sure that each engagement between you and your developer begins with a trial period of up to two weeks. This means that you have time to confirm the engagement will be successful. If you’re completely satisfied with the results, we’ll bill you for the time and continue the engagement for as long as you’d like.
Driven by reusable codes, the use of Java to develop Artificial Intelligence solutions is exploding. USM technologists are experts in providing Robotic Process Automation services that helped several tech organizations generate higher profit margins. VideoPeel is transforming the traditional consumer research by automating the analysis of user generated content to produce dynamic consumer personas and actionable insights. We continually produce high-quality articles, ebooks, and webinars full of helpful information, insights, and practical examples. Join our team and become part of a problem-solving community that’s passionate about making the future tech-powered.
AI and Machine Learning Integration & Technologies
By leveraging the AI service provider’s learnings, experiences, technology, and expertise, clients obtain the essential capability more quickly and at a lesser cost than if they developed the capabilities themselves. If you’re looking for an artificial intelligence development software, SDLC Corp is the company to go to. They were able to develop a customized AI software that fit my business needs perfectly. We create artificial intelligence tools for conversations, like chatbots, Midjouney, chatGPT, and Dall-E, using our proprietary hybrid AI model, which delivers top-notch accuracy in natural language processing and understanding. Use AI to accelerate demand planning, improve demand signals, and tightly integrate cross-functional supply chain processes. Predictive analytics uses statistical algorithms to forecast future trends, which enables businesses to optimize inventory, improve delivery times, increase sales and reduce operational costs.
- Manual and repetitive tasks bored Lepage, so she learned how to automate these types of tasks using databases such as Structured Query Language and Visual Basic for Applications.
- We implement the development strategies into your application and thoroughly test it to ensure correct results.
- In addition, there is concern regarding the fairness and biases of AI algorithms, so the taskforce has been directed to analyze these issues and make recommendations regarding future usage.
- Artificial Intelligence can provide a personal experience to every customer by anticipating their needs.
- Automatic identification of fraudulent actions and notification of clients and financial institutions is made possible using AI and machine learning.
- These insights can provide you with valuable data to make informed decisions about essential company policies and programs.
Lifewatch worked with Unicsoft for 3.5 years, during this time the product was launched and supported for over a year. Unicsoft allocated a team of very professional developers who did a great job for us and we intend to work with Unicsoft more in the future. Move forward with detailed, development-ready specs, time and cost estimates, a launch plan, and a team of Toptal experts ready to execute your plan. I was so impressed with their work that I’ve kept them on board as my go-to experts. Contact Concetto Labs- the top AI App development company to get the desired results. The next stage prioritizes the highest-value AI prospects in light of the bank’s existing procedures and long-term goals.
BIMLIB is pleased to be a partner of Apro and looks forward to a futher fruitful cooperation. Our unique OpenX approach was designed through years of experience to solve the communication, tracking and delivery pitfalls of the popular SCRUM methodology it’s based on. The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its author, and do not reflect the views of the Institution, its management, or its other scholars. Some observers already are worrying that the taskforce won’t go far enough in holding algorithms accountable.
To add to that, AI has allowed financial institutions to automate procedures, which has improved productivity while decreasing operating expenses. Artificial intelligence plays a critical part in transforming the banking sector by altering how banks function, handle client interactions, and process data. AI in banking apps improves operational efficiency, saves costs, and provides more customized experiences to consumers in response to the development of digitization and the rising demand for personalized financial services. When it comes to optimizing operations and providing a better customer experience, full-scale AI implementation in banking aids in various areas, from fraud detection to customer support. They were able to create an artificial intelligence solution that improved my business operations.
How are Toptal developers different?
They can get outside help from a technology firm if they need the necessary in-house expertise. In case of query talk to experts in one of the top machine learning and artificial intelligence companies- A3logics. Artificial Intelligence is based on building smart and highly intelligent machines. On the other hand, machine learning is the subset of artificial intelligence that helps develop AI-driven apps. We have a dedicated data science team at A3logics that helps you develop AI solutions.
The team was managed in a transparent way and we were able to follow the development both in terms of the code and in terms of the user load. I’m very happy with the iOS and Android work Unicsoft performed for me. The quality of code and communications Unicsoft provided has certainly proved they are a capable and trustworthy team of professionals. I would highly recommend them as a highly competent, cost-effective development team.
#7: The Development of Robotic Assistants
They are unlike passive machines that are capable only of mechanical or predetermined responses. Using sensors, digital data, or remote inputs, they combine information from a variety of different sources, analyze the material instantly, and act on the insights derived from those data. With massive improvements in storage systems, processing speeds, and analytic techniques, they are capable of tremendous sophistication in analysis and decisionmaking. I had a wonderful experience working with SDLC Corp for their AI solution provider services.
Each challenge’s level of difficulty varies from project to project and can be planned and estimated in advance by a team of professionals. From candidate screening to direct calls and even interviewing, multiple stages of hiring and onboarding can be fully automated by AI. The number and variety of e-commerce fraud attacks is constantly increasing. Thus, AI can automate the vast majority of repetitive workflows without hardcoding each of them. Quickly reacted to our request and provided an interesting suite of candidates.
Google’s Bard produced factually inaccurate answers in a public demo, costing the company billions of dollars in market value when its shares tumbled. The promise of ChatGPT-like technology is also threatening jobs in some industries due to its low cost, efficiency, and ease of use. The rapid pace at which technology like ChatGPT is developing and becoming embedded with people’s lives could be another “iPhone moment,” Bank of America analysts wrote in a research note on Tuesday. Just as the iPhone led to an explosion in the use of smartphones and phone apps, ChatGPT-like technology is revolutionizing A.I., the bank said in a note on Tuesday.
What is the time frame estimate for artificial intelligence software development with A3logics?
If you need help with AI, ML and Deep learning solutions talk to one of the top machine learning companies- A3logics. In robotics, we develop software for voice recognition, face recognition, sensor data processing, and artificial intelligence development in the context of training robots. Optimizing operations and making flying safer is possible with advanced data analytics and high-quality self-taught automation software solutions.
AI-powered tools for developers can automate some tasks , improve the code quality, or even suggest optimizations in design based on users’ behavior. On the other hand, it requires web apps and systems to be written in a certain way that enables integration with artificial intelligence models. For sure, it is an important trend that will have a significant impact on web development. Your AI Sprint has tested a variety of models that can solve your problem, and at the end, you see which AI-based solution performs best.
ML Research &AI Consulting
From the idea to design, implementation, and support, regardless of industry, scope, or complexity. Faster Processing & Enhanced System Performance with AI & Machine Learning Development company Services. Our expert consulting services at A3logics also help implement the solutions we recommend in simple, cost-effective ways that ensure business continuity. A growing number of businesses & firms are turning to artificial intelligence to extract untapped value from vast quantities of data and increase productivity by orders of magnitude. A self-taught system can search through unstructured historical records more efficiently, crunch numbers more quickly, and automate some of the most time-consuming tasks. Our technologists use multi-layered Artificial Neural Networks to develop & deliver best deep learning solutions to make intelligent decisions quickly.
Microsoft Pix, another popular machine learning tool, uses recent AI developments to select the best three shots out of ten frames captured. The app then automatically deletes the other shots to optimize users’ photographing skills and save space in their smartphone storage. The researchers’ future priorities include the development of wearable mobility aids and technology that can guide people to restrooms and to bed when new AI predicts that it’s the correct time to do so.
Machine Learning Consulting Services
AI chatbots in banking apps stand out as one of the most promising uses of AI. Once deployed, they may operate around the clock, whereas humans are limited to 9 to 5. Furthermore, they maintain gaining knowledge about a customer’s unique consumption habits. Thus, AI in personal banking is highly useful to enhance end-user experiences.


The Square D QO, QOT, QO-AFI, and QO-GFI are plug-on type one-, two- and three-pole thermal-magnetic circuit breakers provide overcurrent protection and switching on ac and dc systems. Plug-on QO circuit breakers are for use in QO load centers, NQ and NQOD panelboards, OEM mounting bases, and Speed-D® switchboard distribution panels. Square D also offers the HomeLine series which is NOT interchangeable with the QO series.
QO® Circuit Breakers
QO® (plug-on) one-, two- and three-pole thermal-magnetic circuit breakers provide overcurrent protection and switching on ac and dc systems. Plug-on QO circuit breakers are for use in QO loadcenters, NQ and NQOD panelboards, OEM mounting bases, and Speed-D® switchboard distribution panels.
QO circuit breakers have an overcenter, trip-free toggle mechanism with quick-make, quick- break action and positive handle indication. The tripping mechanisms in two-and three-pole circuit breakers operate such that an overcurrent on any pole of the circuit breaker will cause all poles of the circuit breaker to open simultaneously. Each pole has an individual thermal-magnetic trip element calibrated for 40°C ambient temperature.
QO circuit breakers have Visi-Trip® trip indication, which provides a visual indication that the circuit breaker has tripped and interrupted the circuit. When the circuit breaker has tripped, the handle assumes a center position and the red Visi-Trip indicator appears in a window in the circuit breaker case. The Visi-Trip indicator is only visible when the circuit breaker has tripped. Trip indication immediately distinguishes the circuit from any other circuit which is merely in the on or off position. The circuit breaker can be reset by pushing the handle to OFF and then to ON.
The circuit breaker must have a voltage rating greater than, or equal to, the system voltage. When a circuit breaker clears an overcurrent, it is done in two steps. First, the current sensing system identifies the overcurrent and releases the tripping mechanism. This results in a parting of the contacts. The circuit breaker must then extinguish the voltage arc across the contacts. If the circuit breaker has the correct voltage rating, it can efficiently extinguish this voltage arc. QO and QOB circuit breakers are rated for use in the following voltage systems:
48 Vdc (10–70 A for 1 and 2 pole circuit breakers, 10–60 A for 3 pole circuit breakers)
QOT Tandem Circuit Breakers
Square D QOT tandem circuit breakers are manufactured so two one-pole, thermal-magnetic circuit breakers occupy only one QO pole space. They are used in applications where circuit loading is light and/or noncontinuous, as in residential applications. QOT circuit breakers are available in 15/15 ampere, 15/20 ampere and 20/20 ampere construction.
Duplex breakers are available in a standard version for modern CTL loadcenters and non-compliant version for non-CTL loadcenters. Modern loadcenters are CTL rated and meet Section 384-15 of the National Electric Code. The use of non-CTL compliant breakers may violate code and UL Listing of certain loadcenters under certain circumstances. QOT circuit breakers have a mounting cam to limit their installation in QO load centers to only those positions having a mounting rail slot. This physically limits the total number of circuit beakers permitted in the panelboard for safe operation.
Each one-pole QOT circuit breaker provides individual switching and tripping action. Individual trip, two-pole circuit with common switching may be assembled by using a handle tie (kit QOTHT) between two adjacent QOT circuit breakers.
Square D QO-AFI arc-fault circuit interrupters (AFCI) quickly detects a wide range of arc-fault conditions, recognises the nature and specific wave-form of an arc fault and trips the circuit breaker. Traditional circuit breakers and fuses are designed to detect overloads and short circuits. Arc-fault circuit breakers are designed to detect overloads, short circuits and arc faults.
An arc-fault circuit breaker opens the circuit and stops the arcing and high intensity heat before a fire is likely to ignite. It is designed with the same quick-open and Visi-Trip® features and reliability of other QO circuit breaker products, fits into most existing Square D load centers, and can generally be used as a direct replacement for a standard Square D circuit breakers. The AFCI overall size is larger than an equivalent QO circuit breaker.
Arc-fault circuit breakers:
Have special microprocessor-based arc identification to differentiate necessary operational arcs (associated with loads such as electric motors, switches and receptacles) from actual arc faults which can cause damage and fires.
Differentiate true arc faults from chopped wave-forms associated with switched-mode power supplies on electrical appliances, computers and lamp dimmers.
Square D QO-GFI Qwik-Gard® Ground-Fault Circuit Interrupters offer a means of providing ground-fault protection for people. Qwik-Gard “people protection” ground-fault circuit interrupters are built as Class A devices in accordance with UL Standard 489 and CSA C22.2 #144 for ground-fault circuit interrupters (GFCIs). Class A devices must trip at 6 milliamperes of ground-fault current and above, and hold below 4 milliamperes of ground-fault current.
Qwik-Gard GFCIs provide the same branch circuit protection as standard QO circuit breakers. They are longer than standard QO circuit breakers, and thus require more gutter space. All QO electrical accessories except shunt trip and all QO mechanical accessories are available for QO-GFI and QOB- GFI circuit breakers.
Qwik-Gard circuit breakers are UL Listed and CSA Certified and available in both one- and two-pole constructions.
Square D QO-CAFI combination Arc-Fault Circuit Interrupters circuit breakers combines GFCI and AFCI in one device, protecting against both Arc Faults and Ground Faults.
The 2014 National Electrical Code (NEC®) now requires both Arc Fault and Ground Fault protection on kitchen and laundry circuits. Before the release of Dual Function Circuit Breakers, the only option to comply with this code was to pair an AFCI circuit breaker with a GFCI receptacle. The Dual Function Circuit breaker combines these two devices into one solution that provides both cost savings and less hassle in installation and maintenance.
For more information about Square D circuit breakers visit Square D's webiste.


Microsoft Exclusively Licenses OpenAI’s Groundbreaking GPT-3 Text Generation Model. According to the official Microsoft Blog, the foremost computing trend is artificial intelligence. Thus as the company develops the ability to deploy huge AI models at scale in a way they can be leveraged by all developers and businesses. However, Artificial intelligence (AI) is gradually becoming an environment where people can build new experiences.
So, to get the AI platform off the ground requires unprecedented computing horsepower. This may, they expanded upon their ongoing partnership with the world-leading AI research organization OpenAI to announce one of the world’s most powerful supercomputers.
OpenAI’s have announced their groundbreaking GPT-3 model- an autoregressive language model that outputs remarkably human-like text. The model happens to be the largest and most advanced language model in the world, clocking in at 175 billion parameters, and is trained on Azure’s AI supercomputer.
Microsoft Exclusively Licenses OpenAI’s Groundbreaking GPT-3 Text Generation Model
According to The Verge, Microsoft’s partnership with San Francisco-based-AI has been for years through OpenAI’s use of the Azure cloud computing platform. Azure is the means through which OpenAI accesses the vast computing resources it needs to train its models.
Ms made a $1 billion investment to become OpenAI’s exclusive cloud provider, a deal that now involves being the exclusive licensee for GPT-3.
OpenAI launches GPT-3, the third iteration of its ever-growing language model, in July. Also, the program and its prior iteration have helped to create some fascinating AI language experiments to date. Thus, it inspires vigorous debate around the ethics of powerful AI is programs which may be used for more vicious purposes. OpenAI initially refusing to publish research about the model with the fear of being misused.
OpenAI said in a blog “ Unlike most AI systems which are designed for one use case, OpenAI’s API today provides a general-purpose text in, test out the interface, allowing users to try it on any English language task. This model is the most powerful behind the API today, with 175 billion parameters.”
Kelvin Scott, Ms chief technology officer announced the deal in a blog post “We see this as an incredible opportunity to expand our Azure-powered AI platforms in a way that democratizes AI technology, enables new products, services, and experiences, and Increases the positive impact of AI at scale.” He continued “Our mission at Microsoft is to empower everyone and organization on the planet to achieve more, so we want to ensure that this AI platform is available to all. Including entrepreneurs, researchers, businesses, hobbyist-to empower their ambitions to create something new and interesting.”
OpenAI said that the deal has no impact on continued access to the GPT-3 model through OpenAI’s API. It’s existing and future users will keep building applications with our API as usual,” which does raise some interesting questions about what exactly Microsoft has acquired here. A Microsoft spokesperson tells The Verge that its exclusive license gives it unique access to the underlying code of GPT-3, which contains technical advancements it hopes to integrate into its products and services.


Project use case
HTML stands for HyperText Markup Language. This is the language used to create web pages. HTML is what makes websites look nice and readable. It tells the browser how to display text, images, and other elements on the page. HTML is also the foundation of many programming languages.
CSS stands for Cascading Style Sheets. This is a style sheet language that allows you to add formatting to your web pages. CSS is often used along with HTML to format web pages.
PHP stands for Personal Home Page. This is a server side scripting language. Server side scripting means that the code is run on the server before being sent back to the client. PHP is commonly used to generate web pages dynamically.
Data science is used to model the relationship between data and reality. To me, this means using structured data to predict outcomes. This is what makes machine learning so exciting. Machine learning involves the use of algorithms to extract patterns from large amounts of data.
Artificial intelligence uses computer programs and algorithms to simulate human thought processes. Deep learning uses neural networks to learn and understand the relationships between data points. When talking about AI, deep learning is often referred to as AI 2.0.


The Functional Architecture of Face Processing in the Primate Brain
Face recognition is a remarkable ability, given the tens of thousands of different faces we can recognize, sometimes even many years after a single encounter. Because of this unique ability, it has been proposed that there may exist specialized neural machinery dedicated to face recognition, as compared to the recognition of non-face objects. Dr. Ungerleider will discuss recent brain imaging studies exploring the functional architecture of face processing in the primate brain. She will focus on the neuronal network properties that mediate the discrimination and recognition of facial identity and expression.


Professor Ole-Christoffer Granmo
Professor Ole-Christoffer Granmo is Director of the Centre for Artificial Intelligence Research (CAIR). His passion for artificial intelligence was lit at the age of ten when he got his first computer and discovered programming. Fascinated by the idea of superintelligence, he obtained his master’s degree in informatics in 1999 and the PhD degree in 2004, both from the University of Oslo, Norway. Granmo develops theory and algorithms for systems that explore, experiment and learn in complex real-world environments. Drawing inspiration from the paradigms of deep reinforcement learning and probabilistic causal reasoning, he seeks to surpass human capability in flexible pattern recognition, model building and reasoning. Within his field of research, Granmo has written more than 115 refereed journal and conference publications.
Prof. Dr. A.E. Eiben
Prof. Dr. A. E. Eiben is leading the Computational Intelligence Group at the Vrije Universiteit Amsterdam, where his research lies in the field of Artificial Intelligence, Artificial Life, Adaptive Collective Systems. His approach to AI is based on Evolutionary Computing. Throughout his carreer he worked on a diverse set of topics from theoretical foundations to artificial societies, multi-sexual reproduction, and applications in health, finance, and traffic management. Lately he is active in the field of Embodied AI, specifically in Evolutionary Robotics. In short, he is working on evolving and learning machines that reproduce, improve themselves, and develop intelligence over time. His most prominent endevours in this direction are the Robot Baby Project and the Autonomous Robot Evolution Project.