Pre-order Hot Toys MMS525 Qui-Gon Jinn Star Wars Episode I The Phantom Menace 1/6th scale Collectible Figure from KGHobby (link HERE)
A venerable if maverick Jedi Master, Qui-Gon Jinn was a student of the living Force. Qui-Gon lived for the moment, espousing a philosophy of “feel, don’t think — use your instincts.” On Tatooine, Qui-Gon discovered a young slave boy named Anakin Skywalker who was strong in the Force. Sensing the boy’s potential, Qui-Gon liberated Anakin from slavery. The Jedi Master presented Anakin to the Jedi Council, but they deemed the boy too old to begin training and dangerously full of fear and anger. They refused to allow Qui-Gon to train Anakin, but rescinded their decision to fulfill Qui-Gon’s dying wish.
Today, Hot Toys is very excited to introduce the new 1/6th scale collectible figure of Qui-Gon Jinn from Star Wars: The Phantom Menace!
Skillfully crafted based on the appearance of Qui-Gon Jinn in the film, this 1/6th scale collectible figure features a newly developed head sculpt, finely tailored Jedi robe and tunic, a desert poncho, a LED light-up lightsaber, a Comlink, a grappling hook, a hologram projector with interchangeable holograms, and a themed figure base!
Hot Toys MMS525 Star Wars: The Phantom Menace 1/6th scale Qui-Gon Jinn Collectible Figure specially features: Authentic and detailed likeness of Liam Neeson as Qui-Gon Jinn in Star Wars: Episode I – The Phantom Menace | Movie-accurate facial expression with detailed wrinkles, beard, and skin texture | Approximately 32 cm tall Body with over 30 points of articulations | Eight (8) pieces of newly sculpted interchangeable hands including: pair of hands for holding lightsaber, left hand for holding holoprojector, Two (2) gesture left hands, Force-using right hand, right hand for holding comlink, relaxed right hand
Costume: grey-colored poncho with weathering effects, brown-colored Jedi robe, beige-colored tunic with belt, beige-colored under tunic, beige-colored sleeveless tunic, beige-colored arm wraps, beige-colored interchangeable arm wrap for LED left arm with lightsaber, brown leather-like belt with lightsaber holster, brown-colored pants, brown leather-like boots
Weapons: LED-lighted green lightsaber (green light, battery operated), green lightsaber blade in motion (attachable to the hilt), lightsaber hilt
Accessories: Comlink, grappling hook, holoprojector, hologram figure of Mace Windu, hologram figure of Yoda, hologram figure of Naboo Royal Starship, Figure stand with Qui-Gon Jinn nameplate and movie logo
Release date: Approximately Q1 – Q2, 2020 Continua a leggere
Preorder from KGHobby (link HERE)
The Witcher is an action role-playing game and the story takes place in a medieval fantasy world and follows Geralt of Rivia, one of a few traveling monster hunters who have supernatural powers, known as Witchers.
The game tells the story of Geralt of Rivia, a Witcher – a genetically enhanced human with special powers trained to slay monsters. The Witcher contains three different paths, which affect the game’s storyline. These paths are: alliance with the Scoia’tael, a guerrilla freedom-fighting group of Elves and other non-humans; alliance with the Order of the Flaming Rose, whose knights protect the country of Temeria; or alliance with neither group to maintain “Witcher neutrality”.
MT TOYS 1/6th scale The White Wolf 12-inch Collectible Figure PARTS LIST: Hunter Head Sculpt, 12-inch Figure Body, Wolf Pendant, Die-cast Bronze Sword, Die-cast Silver Sword, Scabbard x2, Vest, White top, Pants, Arm Armor x2, Leather Pouch, Leather Belt, Medicine Bottles x3, Dagger, Dagger Scabbard, Hunter Hook, Boots, Gloved hands x3, Hands x8
Posted by Mario Lučić, Research Scientist and Marvin Ritter, Software Engineer, Google AI Zürich
Generative adversarial networks (GANs) are a powerful class of deep generative models.The main idea behind GANs is to train two neural networks: the generator, which learns how to synthesise data (such as an image), and the discriminator, which learns how to distinguish real data from the ones synthesised by the generator. This approach has been successfully used for high-fidelity natural image synthesis, improving learned image compression, data augmentation, and more.
|Evolution of the generated samples as training progresses on ImageNet. The generator network is conditioned on the class (e.g., “great gray owl” or “golden retriever”).|
For natural image synthesis, state-of-the-art results are achieved by conditional GANs that, unlike unconditional GANs, use labels (e.g. car, dog, etc.) during training. While this makes the task easier and leads to significant improvements, this approach requires a large amount of labeled data that is rarely available in practice.
In “High-Fidelity Image Generation With Fewer Labels“, we propose a new approach to reduce the amount of labeled data required to train state-of-the-art conditional GANs. When combined with recent advancements on large-scale GANs, we match the state-of-the-art in high-fidelity natural image synthesis using 10x fewer labels. Based on this research, we are also releasing a major update to the Compare GAN library, which contains all the components necessary to train and evaluate modern GANs.
Improvements via Semi-supervision and Self-supervision
In conditional GANs, both the generator and discriminator are typically conditioned on class labels. In this work, we propose to replace the hand-annotated ground truth labels with inferred ones. To infer high-quality labels for a large dataset of mostly unlabeled data, we take a two-step approach: First, we learn a feature representation using only the unlabeled portion of the dataset. To learn the feature representations we make use of self-supervision in the form of a recently introduced approach, in which the unlabeled images are randomly rotated and a deep convolutional neural network is tasked with predicting the rotation angle. The idea is that the models need to be able to recognize the main objects and their shapes in order to be successful on this task.
|An unlabeled image is randomly rotated and the network is tasked with predicting the rotation angle. Successful models need to capture semantically meaningful image features which can then be used for other vision tasks.|
We then consider the activation pattern of one of the intermediate layers of the trained network as the new feature representation of the input, and train a classifier to recognize the label of that input using the labeled portion of the original data set. As the network was pre-trained to extract semantically meaningful features from the data (on the rotation prediction task), training this classifier is more sample-efficient than training the entire network from scratch. Finally, we use this classifier to label the unlabeled data.
To further improve the model quality and training stability we encourage the discriminator network to learn meaningful feature representations which are not forgotten during training by means of an auxiliary loss we introduced previously. These two advancements, combined with large-scale training lead to state-of-the-art conditional GANs for the task of ImageNet synthesis as measured by the Fréchet Inception Distance.
|Given a latent vector the generator network produces an image. In each row, linear interpolation between the latent codes of the leftmost and the rightmost image results in a semantic interpolation in the image space.|
Compare GAN: A Library for Training and Evaluating GANs
Cutting-edge research on GANs is heavily dependent on a well-engineered and well-tested codebase, since even replicating prior results and techniques requires a significant effort. In order to foster open science and allow the research community benefit from recent advancements, we are releasing a major update of the Compare GAN library. The library includes loss functions, regularization and normalization schemes, neural architectures, and quantitative metrics commonly used in modern GANs, and now supports:
- Training on GPUs and TPUs.
- Lightweight configuration via Gin (examples).
- A plethora of data sets via the TensorFlow datasets library.
Conclusions and Future Work
Given the growing gap between labeled and unlabeled data sources, it is becoming increasingly important to be able to learn from only partially labeled data. We have shown that a simple yet powerful combination of self-supervision and semi-supervision can help to close this gap for GANs. We believe that self-supervision is a powerful idea that should be investigated for other generative modeling tasks.
Work conducted in collaboration with colleagues on the Google Brain team in Zürich, ETH Zürich and UCLA. We would like to thank our paper co-authors Michael Tschannen, Xiaohua Zhai, Olivier Bachem and Sylvain Gelly for their input and feedback. We would like to thank Alexander Kolesnikov, Lucas Beyer and Avital Oliver for helpful discussion on self-supervised learning and semi-supervised learning. We would like to thank Karol Kurach and Marcin Michalski for their major contributions to the Compare GAN library. We would also like to thank Andy Brock, Jeff Donahue and Karen Simonyan for their insights into training GANs on TPUs. The work described in this post also builds upon our work on “Self-Supervised Generative Adversarial Networks” with Ting Chen and Neil Houlsby.
Posted by Chris Shallue, Senior Software Engineer and George Dahl, Senior Research Scientist, Google AI
Over the past decade, neural networks have achieved state-of-the-art results in a wide variety of prediction tasks, including image classification, machine translation, and speech recognition. These successes have been driven, at least in part, by hardware and software improvements that have significantly accelerated neural network training. Faster training has directly resulted in dramatic improvements to model quality, both by allowing more training data to be processed and by allowing researchers to try new ideas and configurations more rapidly. Today, hardware developments like Cloud TPU Pods are rapidly increasing the amount of computation available for neural network training, which raises the possibility of harnessing additional computation to make neural networks train even faster and facilitate even greater improvements to model quality. But how exactly should we harness this unprecedented amount of computation, and should we always expect more computation to facilitate faster training?
The most common way to utilize massive compute power is to distribute computations between different processors and perform those computations simultaneously. When training neural networks, the primary ways to achieve this are model parallelism, which involves distributing the neural network across different processors, and data parallelism, which involves distributing training examples across different processors and computing updates to the neural network in parallel. While model parallelism makes it possible to train neural networks that are larger than a single processor can support, it usually requires tailoring the model architecture to the available hardware. In contrast, data parallelism is model agnostic and applicable to any neural network architecture – it is the simplest and most widely used technique for parallelizing neural network training. For the most common neural network training algorithms (synchronous stochastic gradient descent and its variants), the scale of data parallelism corresponds to the batch size, the number of training examples used to compute each update to the neural network. But what are the limits of this type of parallelization, and when should we expect to see large speedups?
In “Measuring the Effects of Data Parallelism in Neural Network Training“, we investigate the relationship between batch size and training time by running experiments on six different types of neural networks across seven different datasets using three different optimization algorithms (“optimizers”). In total, we trained over 100K individual models across ~450 workloads, and observed a seemingly universal relationship between batch size and training time across all workloads we tested. We also study how this relationship varies with the dataset, neural network architecture, and optimizer, and found extremely large variation between workloads. Additionally, we are excited to share our raw data for further analysis by the research community. The data includes over 71M model evaluations to make up the training curves of all 100K+ individual models we trained, and can be used to reproduce all 24 plots in our paper.
Universal Relationship Between Batch Size and Training Time
In an idealized data parallel system that spends negligible time synchronizing between processors, training time can be measured in the number of training steps (updates to the neural network’s parameters). Under this assumption, we observed three distinct scaling regimes in the relationship between batch size and training time: a “perfect scaling” regime where doubling the batch size halves the number of training steps required to reach a target out-of-sample error, followed by a regime of “diminishing returns”, and finally a “maximal data parallelism” regime where further increasing the batch size does not reduce training time, even assuming idealized hardware.
Although the basic relationship between batch size and training time appears to be universal, we found that the transition points between the different scaling regimes vary dramatically across neural network architectures and datasets. This means that while simple data parallelism can provide large speedups for some workloads at the limits of today’s hardware (e.g. Cloud TPU Pods), and perhaps beyond, some workloads require moving beyond simple data parallelism in order to benefit from the largest scale hardware that exists today, let alone hardware that has yet to be built. For example, in the plot above, ResNet-8 on CIFAR-10 cannot benefit from batch sizes larger than 1,024, whereas ResNet-50 on ImageNet continues to benefit from increasing the batch size up to at least 65,536.
If one could predict which workloads benefit most from data parallel training, then one could tailor their workloads to make maximal use of the available hardware. However, our results suggest that this will often not be straightforward, because the maximum useful batch size depends, at least somewhat, on every aspect of the workload: the neural network architecture, the dataset, and the optimizer. For example, some neural network architectures can benefit from much larger batch sizes than others, even when trained on the same dataset with the same optimizer. Although this effect sometimes depends on the width and depth of the network, it is inconsistent between different types of network and some networks do not even have obvious notions of “width” and “depth”. And while we found that some datasets can benefit from much larger batch sizes than others, these differences are not always explained by the size of the dataset—sometimes smaller datasets benefit more from larger batch sizes than larger datasets.
|Left: A transformer neural network scales to much larger batch sizes than an LSTM neural network on the LM1B dataset. Right: The Common Crawl dataset does not benefit from larger batch sizes than the LM1B dataset, even though it is 1,000 times the size.|
Perhaps our most promising finding is that even small changes to the optimization algorithm, such as allowing momentum in stochastic gradient descent, can dramatically improve how well training scales with increasing batch size. This raises the possibility of designing new optimizers, or testing the scaling properties of optimizers that we did not consider, to find optimizers that can make maximal use of increased data parallelism.
Utilizing additional data parallelism by increasing the batch size is a simple way to produce valuable speedups across a range of workloads, but, for all the workloads we tried, the benefits diminished within the limits of state-of-the-art hardware. However, our results suggest that some optimization algorithms may be able to consistently extend the perfect scaling regime across many models and data sets. Future work could perform the same measurements with other optimizers, beyond the few closely-related ones we tried, to see if any existing optimizer extends perfect scaling across many problems.
The authors of this study were Chris Shallue, Jaehoon Lee, Joe Antognini, Jascha Sohl-Dickstein, Roy Frostig and George Dahl (Chris and Jaehoon contributed equally). Many researchers have done work in this area that we have built on, so please see our paper for a full discussion of related work.
Pre-order COOMODEL 1/6 VICE CITY VC001 THE DETECTIVE W FIGURE from KGHobby (link HERE)COOMODEL X OUZHIXIANG NO.VC001 1/6th scale VICE CITY – THE DETECTIVE W (STANDARD EDITION) specially features: smoking head of human form, grim head of human form, nor… Continua a leggere
I’ve just finished writing another book on PowerShell. The book looked at a number of core Windows Features and components, from AD, DHCP/DNS, SMB file sharing and FSRM, Hyper, V and more. Having gone through the process of witting over 125 scripts using a dozen Windows Server 2018 features has given me a perspective on both PowerShell in use today, and in what it takes, what you really need to know in order to progress from a Google-Engineering assisted journey man into a PowerShell master. There is absolutely NOTHING wrong with using Google to get the job done. And for many their career path possibly precludes acquiring deep skills. But if you are the folks who aspire, please read on!
So what do you need to know, to know how to do? The following list is not in any order:
- Understand and be able to use the PowerShell language - the starting point, for me, is that you know the PowerShell language and can use it. You should understand the core concepts of cmdlets, objects, and the pipeline. You should be familiar the the core approach to discovery (Get-Module, Get-Command, Get-Help, and Get-Member). You need to know how each language feature works and be able to leverage it in your scripts. Knowing the internal architecture of PowerShell is also almost expected.
- Understand the .NET Framework. Powershell is built on top of .NET – cmdlets work by using .NET. Get-Process, for example, just calls [System.Diagnostics.Process]::GetProcesses(). Also known as a static method (GetProcesses()) on a .NET Class (Systems.Diagnostics.Process). You should understand the architecture of .NET (CLR, BCL, IL and JIT Compilation, .NET Security, and more). The .NET Framework can often provide functionality for which there are no cmdlets. For example, there a number of .NET classes useful for localisation. Time zones, clock types, DST/ST, etc are all a method call away and therefore easy to use if you know how.
- Understand how to read C# and be able to convert C# to PowerShell. There is a feast of wonderful examples of more obscure tasks often written in C#. You should be able to read the C# sufficiently to see how the code is doing things, and be able to concert simple snippets into working PowerShell Code. Knowing enough VB.NET to convert it into PowerShell is also a useful skill.
- Understand COM and COM objects. There are a number of features that make use of COM. The Microsoft Office products can each be automated using PowerShell’s COM interop features. The Performance Logging and Alerting subsystem makes use of COM. You use PowerShell’s New-Object cmdlet to instantiate a COM object to specify PLA data collector sets.
- Know how to use XML. Some features such as the Task Scheduler make use of XML. You should know how to use the XML emitted by windows as well as knowing how to manage XML documents and the DOM. XPath is also a useful skill. The FSRM, for example, produces reports. The report format is fixed and cannot be modified. But the FSRM also produces XML files containing the report’s raw data for you to format to your own needs. PowerShell also makes use of XML for default object formatting. which you can customise to change how PowerShell formats objects.
- Know how PowerShell modules work. Cmdlets are delivered in modules and you can write your own. Both DSC and JEA leverage modules. You should know where modules are stores, how PowerShell finds them and how it builds the Module cache, what a manifest is.
- JEA – Also known as Just Enough Administration. It’s a neat feature that enables you to provide delegated permissions to do just those things necessary for a person’s job and nothing more. This is a very useful security feature of Powershell that appeals to large and distributed organisations.
- Understand how to implement DSC. DSC is a great way to configure hosts and to ensure they stay configured. You should know about DSC resources, setting up DSC pull servers (SMB and Web), and DSC reporting and error logging. You should also know how DSC resources work as well as how to write your own DSC resources)
- Master Remoting – This is a rich topic area. You should understand the PowerShell Remoting stack (including PSRM, SOAP, WinRM), how end points work and how to create a constrained end point.
- Understand at Depth Core Windows Features. I suppose it’s obvious, but to be a PowerShell master you have to be able to apply your skills to Windows. You should really understand AD (and GPO), SMB (SMB3 SOFS, Clustering and Hyper-Converged S2D), Containers and Docker, Hyper-V (and maybe VMware too!), TCP/IP Networking, Disk/FIle Storage, PLA, Task Scheduler, and probably more.
- Leverage Azure – Organisations are increasingly moving to the cloud and knowing Azure (or AWS) is also an important skill. With Azure, you should be able to build IAAS objects in cloud including web sites, VMs, and Virtual networks. You should also be able to manage Azure Storage and Content distribution.
- Be competent at Windows Troubleshooting – There are a variety of good PowerShell tools that assist in Troubleshooting, particularly network troubleshooting. These tools really are second nature to a PowerShell master. And to be a good trouble-shooter you really need to understand what you are troubleshooting. Knowing how to leverage the information in the event logs is also critical. You should become very familiar with docs.Microsoft.com. And if you ever work out how to fully automate the Windows trouble-shooters – let me know.
- Use PowerShell Core and VSCode – PowerShell Core 6 is almost a re-invention of PowerShell. Cross-platform, open source, based on .NET Core which is also open source along with a totally new development tool (VS Code). Arguably,6.1 and 6.2 are not quite ready for hard core usage across all features. But it’s close – I am now using the developing 6.2 and VS Code in preference to the ISE and Windows PowerShell. My Grateful Dead scripts even work in PS Core! There are a number of features that do not work with PowerShell Core. Today, for example, DSC and Windows Forms are not supported (although 6.3 should support Windows.Forms!).
So a baker’s dozen of things you really need to know, and know how to do with PowerShell.
Posted by Sella Nevo, Senior Software Engineer and Rainier Aliment, Program Manager
Recently, we hosted the Google Flood Forecasting Meets Machine Learning workshop in our Tel Aviv office, which brought hydrology and machine learning experts from Google and the broader research community to discuss existing efforts in this space, build a common vocabulary between these groups, and catalyze promising collaborations. In line with our belief that machine learning has the potential to significantly improve flood forecasting efforts and help the hundreds of millions of people affected by floods every year, this workshop discussed improving flood forecasting by aggregating and sharing large data sets, automating calibration and modeling processes, and applying modern statistical and machine learning tools to the problem.
|Panel on challenges and opportunities in flood forecasting, featuring (from left to right): Prof. Paolo Burlando (ETH Zürich), Dr. Tyler Erickson (Google Earth Engine), Dr. Peter Salamon (Joint Research Centre) and Prof. Dawei Han (University of Bristol).|
The event was kicked off by Google’s Yossi Matias, who discussed recent machine learning work and its potential relevance for flood forecasting, crisis response and AI for Social Good, followed by two introductory sessions aimed at bridging some of the knowledge gap between the two fields – introduction to hydrology for computer scientists by Prof. Peter Molnar of ETH Zürich, and introduction to machine learning for hydrologists by Prof. Yishay Mansour of Tel Aviv University and Google
|An overview of research areas in flood forecasting addressed in the workshop.|
Presentations from the research community included:
- Dr. Dhanya C. T. of IIT Delhi gave a talk on satellite precipitation error characterization.
- Adarsh M. S., Assistant Director of the Indian Ministry of Water Resources presented India’s Central Water Commission’s role and challenges.
- Prof. Andras Bardossy of the University of Stuttgart discussed variation in discharge series and the challenges this presents.
- Frederik Kratzert of Johannes Kepler University presented recent work on hydrologic modeling using LSTMs.
- Prof. Paul Bates of the University of Bristol gave a keynote on the potential uses of machine learning in inundation modelling.
- Prof. Emmanouil Anagnostou of the University of Connecticut spoke about hyper-resolution hydrologic simulations at global-scale.
- Prof. Efrat Morin of the Hebrew University highlighted flood prediction challenges in dry climate regions.
- Dr. Zachary Flamig of the University of Chicago presented NASA’s new global flash flood prediction project.
Alongside these talks, we presented the various efforts across Google to try and improve flood forecasting and foster collaborations in the field, including:
- Vova Anisimov presented our progress in hydraulic modeling.
- Ami Weisel presented our research on remote discharge estimation.
- Stephan Hoyer presented our work on data-driven discretization approach to solving partial differential equations.
- Jason Hickey presented our efforts using machine learning for precipitation prediction.
- Avinatan Hassidim presented lessons learned from previous projects in Google, and how they apply to our flood forecasting efforts.
Additionally, at this workshop we piloted an experimental “ML Consultation” panel, where Googlers Gal Elidan, Sasha Goldshtein and Doron Kukliansky gave advice on how to best use machine learning in several hydrology-related tasks. Finally, we concluded the workshop with a moderated panel on the greatest challenges and opportunities in flood forecasting, with hydrology experts Prof. Paolo Burlando of ETH Zürich, Prof. Dawei Han of the University of Bristol, Dr. Peter Salamon of the Joint Research Centre and Dr. Tyler Erickson of Google Earth Engine.
Flood forecasting is an incredibly important and challenging task that is one part of our larger AI for Social Good efforts. We believe that effective global-scale solutions can be achieved by combining modern techniques with the domain expertise already existing in the field. The workshop was a great first step towards creating much-needed understanding, communication and collaboration between the flood forecasting community and the machine learning community, and we look forward to our continued engagement with the broad research community to tackle this challenge.
We would like to thank Avinatan Hassidim, Carla Bromberg, Doron Kukliansky, Efrat Morin, Gal Elidan, Guy Shalev, Jennifer Ye, Nadav Rabani and Sasha Goldshtein for their contributions to making this workshop happen.
In testing any new feature, one technique for getting users to use (and test) the feature is known as feature flags. Essentially these are settings (flags) that signal you should gain access to those experimental features. The allows a user to opt-in to testing the new features. Thus is a big application, such as PowerShell, most users just use the published feature set. But if you know how, you can turn on some interesting new features. And of course if you do not like a given experimental feature, you can turn it off. Let’s look at hot oaccess these features. In this blog post, I am using PowerShell Core 6.2.0-rc.1. If you install different versions, your mileage is going to vary!
Finding Experimental features
Finding experimental features is pretty easy. Hey – this is PowerShell and you should know what to do). Like this:
PS [C:\foo> ]> Get-ExperimentalFeature
Name Enabled Source Description
—- ——- —— ———–
PSCommandNotFoundSuggestion False PSEngine Recommend potential commands based on fuzzy search on a CommandNotFoundException
PSImplicitRemotingBatching False PSEngine Batch implicit remoting proxy commands to improve performance
PSTempDrive False PSEngine Create TEMP: PS Drive mapped to user’s temporary directory path
PSUseAbbreviationExpansion False PSEngine Allow tab completion of cmdlets and functions by abbreviation
So these four experimental feature present on 6.2.0-rc.1 all look pretty cool to me. The usefulness of these may vary. For me tablcompletion of cmdlet names using abbreviations could interesting. Potentialy really useful so worth looking at even though as I think about it – if the alias is any good, it’s wired into my fingers thus it may be a feature that I never use. Creating the TEMP: folder is something I would take advantage of. The PSCommandNotFoundSuggestion just helps IT Admins to find what they need. And the batching of implicit remoting commands could be very useful if you are, for example, managing Exchange On-Line using local PowerShell and importing the session.
Enabling Experimental Features
Again – this is PowerShell so: Simples:
PS [C:\foo> ]> Get-ExperimentalFeature | Enable-ExperimentalFeature
WARNING: Enabling and disabling experimental features do not take effect until next start of PowerShell.
WARNING: Enabling and disabling experimental features do not take effect until next start of PowerShell.
WARNING: Enabling and disabling experimental features do not take effect until next start of PowerShell.
WARNING: Enabling and disabling experimental features do not take effect until next start of PowerShell.
So very simple to add in. Due to how these features are implemented you nee to restart Pwsh before you can access the fetures:
Using Experimental Features:
The Command not found suggestion feature is nice. After enabling, PowerShell does a better job of handling typos, like this:
PS [C:\foo> ]> get-chliditem
get-chliditem : The term ‘get-chliditem’ is not recognized as the name of a cmdlet, function, script file, or operable program.
Check the spelling of the name, or if a path was included, verify that the path is correct and try again.
At line:1 char:1
+ CategoryInfo : ObjectNotFound: (get-chliditem:String) , CommandNotFoundException
+ FullyQualifiedErrorId : CommandNotFoundException
Suggestion [4,General]: The most similar commands are: Get-ChildItem, Get-ChildItem2.
Nice. It could be really useful where you have long cmdlet names
Disabling Experimental Features
Needless to say, disabling them is simple too: Use the DisableExperimentalFeature cmdlet to disable the commands.
Experimental features are PowerShell Core features that may or may not be added to future versions of PowerShell Core. They are easy to enable, consume, and disable as you wish but use at your own risk.