Better Breadcrumbs
By Daniel Wood, 4 October 2018
Introduction
Breadcrumb menus are great. They tell the user a lot about where they are within a solutions hierarchy. They also provide a really quick and easy way to navigate up/down that hierarchy if needed. In the FileMaker world, people have been making breadcrumb menus in various forms for a while, the most common implementation is that of a repeating fields, button bars, or in the case of vertical menus, a portal.
And while all of these methods are workable, they tend to have limitations when it comes to 2 aspects - the visual quality of the menu, and the ability to customise and extend the menu. So what do we mean by these? We’ll start off by giving an example of a breadcrumb menu built using a button bar, discuss some of its limitations, and then present our alternative implementation using a tab control object.
Example file time!
Rather than wait til the end to check out the demo, we strongly recommend you download and explore the example file as you read. This will help you follow along with the content of the article and help you to understand what we are talking about.
A typical breadcrumb menu
Here is an example of a standard breadcrumb menu. This is a location based menu, as the items in the menu are locations within the solution the user can navigate to.
The user currently resides at the right-most location in the menu, and as you scan to the left you can work your way back up the navigation hierarchy all the way to home screen. These navigation elements are clickable, so the user is free to traverse back up the hierarchy to any point they wish.
Building this in FileMaker
Many peoples first instinct would be to use a button bar to design and build a breadcrumb menu. It has a number of properties that suit a breadcrumb menu:
- Multiple segments, each could be a step in the hierarchy
- Clickable, as each segment is essentially a button
- You can calculate the text to appear in each segment.
But here’s the problem. Button bars are of a fixed width, and the segments within the button bar are all proportional in width to the overall width of the bar itself. So if you have a 100pt wide button bar, with 10 segments, then each segment will be 10 pts wide. If you extend the width of the bar to 200pts, then each segment grows to 20pts in width. You have no control over the width of each individual segment.
So what does this mean for us in real terms?
Here is a crack at building a breadcrumb menu with a button bar. The issue we have is that each segment has a variable amount of text, yet we can only have a single width per segment. Add to this the fact that typical breadcrumb menus have a divider between each element, and you end up with a pretty average looking menu.
This is what it looks like in layout mode, to further illustrate whats going on.
Now, we have seen people come up with attempts to work around this fixed width segment issue. Some involve creating button bar segments based on calculations, where the text inside each segment is padded with spaces to make it a certain width, while others involve starting off with hundreds of small segments, and programmatically removing certain segments and padding others. The simple fact is these are all complex and a real pain to work with, and you still do not achieve a really beautiful result.
What about a tab control?
What’s that, I hear you ask? A tab control? Surely a tab is the last object you’d think of to build a breadcrumb menu right? Well maybe, but the fact is tab controls are the perfect layout object for building them (short of an actual breadcrumb menu layout object!).
The reason why tab controls are so great for this, is the simple fact that the width of each tab control name is variable in width. This means it doesn’t matter how much or how little text goes into each tab name, they won’t all end up the same width.
This is a tab control, designed to look like a breadcrumb menu. Looks pretty nice doesn’t it. Notice how all of the spacing between the dividers and the items are all consistent. So how is this done?
Here is the same tab control object highlighted in layout mode. The height of the overall object has been reduced such that there is actually no content space, it’s just the height of the tab names themselves. We aren’t going to be using this object for placing other objects in, we are only concerned with the names.
Let’s look at the tab control setup next.
Interesting! What we can see here is that the odd positions in the tab control are given the names of the items in the menu. Whilst the even positions are used for dividers. This is a key concept in our technique for building the menu - odd spaces are for items, even spaces are for separators.
The above setup is kind of useless in an actual solution because it is so hard-coded. Ideally you want the menu to be dynamic, and have elements add/remove as you traverse up/down the navigation hierarchy of your solution, but at this point we’re simply showing you the building blocks for how we structure the object.
We use the “Label width + Margin of” option for tab width, this allows tabs to grow as more text is added, whilst maintaining an even spacing between items and dividers. We are using an ascii character of a right arrow for the divider.
Formatting items
You’ll note that the first three items in the menu are underlined. This is to give visual indication to the user that these are clickable. The right-most element is not underlined, suggesting that is the screen they are current on, and there is no need for them to click that link.
Visual design is achieved through conditional formatting of each individual tab control. In this very basic example, the condition for the first 3 items is simply “true” and we format them to underlined. Again in reality we want to be a bit more dynamic in our conditional formatting, which we’ll cover later.
For a navigation breadcrumb, you may actually wish to simply go with a hard-coded menu such as the one above, and just adjust its display for each layout it appears on, and indeed this may be the easiest implementation. Other implementations may require a more soft-coded dynamic approach.
A simple example
In the example file we start off with a simple wizard example. Here we use a breadcrumb menu to indicate position in a step by step wizard.
This wizard has 5 steps, and the user will work their way through the wizard, and continue to the next section by clicking a button. The menu itself is not clickable, it exists purely as a visual aid to inform the user of their progress in the wizard.
We use the tab control for display of the menu, and we are using a slide-control beneath it for the wizard itself. So, the slide control has 5 panels and each panel is named Wizard_1 through Wizard_5.
The tab control setup is as follows:
Pretty simple stuff. Again odd positions for items, even positions are separators. Because the size of this wizard is known, we only need to add however many tabs are required for each step.
Navigation through the wizard is done by running a script. The script takes as a parameter a direction, be it forward or back. Depending on which direction, it updates the value of a global variable $$WIZARD_POSITION. We use this global variable to help us know which step of the wizard we are on. The script then simply navigates to the next or previous slide panel.
The breadcrumb menu now has to update visually to reflect the users position also. We know the position of the user based on the number in the global variable which will be between 1 and 5. The visual updating is done via conditional formatting, so let's take a look at that:
This is really easy. What we are looking at is the conditional formatting rule for the second position “Your Details”. We’re saying that if the user is at this position, or has gone past this position already, that it should be coloured.
Here the user is on step 4 “Interests”, and so the conditional formatting of items 1 through 4 are evaluated to true, and are coloured bold and green.
For the dividers, they are irrelevant in our example, so we can either always evaluate their conditional formatting to true (and assign them some property, in this case grey colour), or you can leave them without conditional formatting, in which case they will inherit the default formatting of the tab control object.
In this example, you’ll note that the text is black, and the dividers are grey, so we have a difference in formatting of the 2 types of tabs. In the interests of simplicity, we make the default tab text colour black, and we have applied conditional formatting to all dividers, to change them to grey.
Adding action to navigation items
More often than not, you want the user to be allowed to click an item in the navigation menu and run a script accordingly. We can achieve this in tab controls by using the OnPanelSwitch object trigger.
Here is the same wizard, although this time all sections can be navigated to at any point in time.
We start by altering the formatting so that all objects are underlined to begin with, indicating that they can be clicked. The other conditional formatting properties are the same as in the previous example, if the user is on a position, or that position is to the left of where the user currently is, we make it bold and green.
If we apply an OnPanelSwitch trigger to the tab control object, then our script will run regardless of which tab is chosen. An important piece of information we will use in the script is the position of the tab the user has clicked. This can be found by evaluating the first value in the function Get ( TriggerTargetPanel ).
There are 2 possible situations here. Firstly, the user may have clicked a divider. Our script will still run in this instance, so we must handle this situation. Recall all even positions are dividers, so we can check whether the clicked position is even. If it is then we return a FALSE result from the script, and the divider tab is not navigated to.
The only other scenario is the user has clicked on an actual item that they can navigate to. This will be an odd number. We must translate this number into the actual wizard position. We need to do this because of the dividers, they offset the clicked item.
To illustrate this consider clicking on “Immediate Family”. You know that this is the third position in the wizard, but it is actually the 5th position in the tab control. So we need to write a translation between the position chosen and the wizard position. It’s pretty straightforward and simply Ceiling ( $PositionClicked / 2 ). In our example, this would be 5/2 = 2.5, and taking the ceiling of this gives us a wizard position of 3.
Now that we know the wizard position, it’s just a case of setting our location to that value, and going to that sliding panel object, all done !
You can indeed write your own script to cater for any positional click in your menu, regardless of what you are using your menu for.
Abstracting item names into a table
Often times your wizard or menu items will exist in a table as records because you need to customise them, or build different menus for different purposes. In this example we’re going to show that you can still use the breadcrumb menu in this fashion.
Here is a table of records, each for a different section in the breadcrumb menu, and below is the breadcrumb menu. The 2 important bits of information in the table are the name of the item, and its position in the menu.
The beauty of using a tab control really stands out when using an abstracted menu like this. The menu will simply expand to accommodate variable lengths of text. In order for things to work smoothly there are just two things you need to be aware of:
- Make the initial width of the tab object wide enough to cater for a worst case scenario length of menu.
- Add enough tab control objects so that you are sure you have enough menu positions to cater for all the items that may end up in the menu.
Here is the tab control setup of this abstracted menu. Wow things are really getting interesting now! What you see there is a custom function which we have named @BREADCRUMB. It takes 2 parameters. The first is a keyword identifying which records in our wizard setup table to retrieve, and the second is the order number to retrieve. The 5 items in our table are all of type “Abstracted”, and are all numbered 1 through 5.
Here’s the custom function. It’s a simple executeSQL query where we retrieve names of items based on their type and order number, again nothing magical here just standard FileMaker.
The rest of the implementation is no different to our other examples. Conditional formatting for the items, and a script trigger for navigation.
Abstracting the formatting as well as the names
In this last example we show how you can tailor the formatting of individual items in the menu as well as their names. Now you could achieve this with conditional formatting again, but if you want a specific item to have a specific formatting you may wish to abstract this into a table of records to be based on actual items, rather than position in the menu.
This is a very similar example to the one above, with a slight exception that we have an additional field in our table containing an RGB function for the colour we want our item to be.
Here is the tab control setup for this example:
We have added in 9 different tabs here. In fact we add more than required in case more are needed. Because the items are abstracted to a menu, but adding more tabs, we ensure we don’t have to come back and potentially add more in future.
The other interesting thing to notice here is that we no longer are adding dividers into the even positions. It’s all just calls to a custom function called @BREADCRUMB_Formatted. This function is identical in behaviour to the earlier one, but this function does a couple more things
- If the order number passed through is even, it returns the divider character
- If the order number passed through is odd, it obtains the name of that item from corresponding record.
- It also obtains the formatting properties from the record, and applies them to the name, using the Evaluate function.
- It also determines using the wizard position $$WIZARD_POSITION whether to format the item, or whether to not format
So in this case, we are not using conditional formatting to determine whether to format an item or not, it is entirely done within the custom function. The formatting properties we use comes from the record itself.
The end result of this is that because we are only displaying text for items, we have full formatting control over how that looks using the text formatting functions. Here we are using slightly different colours for each item.
Tabs are awesome
The tab control object is just one of those cool objects that just keeps giving. We really love these breadcrumb menus and feel they have a really useful place in solutions. They can also be now made to look really professional and behave just like a breadcrumb menu should as well as being very easy to customise and format.
Example file again!
As with all of our articles we produce we like to provide a detailed example file to go along with it. It’s not enough to just read how something is done, you should be able to see it in action and explore how it works yourself. Please find attached the example file below.
Credits
We'd like to thank Greig Jackson here at Digital Fusion for coming up with this method — nice work!
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Еще одно преимущество — разнообразие функций. Современные модели оснащаются гидромассажем, подсветкой, встроенным радио и даже парогенераторами, превращающими кабину в мини-сауну. Это делает их не только средством гигиены, но и способом расслабления после долгого дня. Также душевые кабины легко чистить, а их закрытая конструкция предотвращает разбрызгивание воды по всей комнате.
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Душевые кабины бывают разных типов, и выбор зависит от ваших потребностей и особенностей помещения. Самый простой вариант — это открытые кабины. Они состоят из поддона и стенок, которые примыкают к стенам ванной комнаты. Такие модели недорогие и подходят для маленьких пространств, но они не обеспечивают полной герметичности.
Закрытые душевые кабины — более популярный выбор. Они имеют крышу и полностью изолированы, что позволяет сохранять тепло и использовать дополнительные функции, такие как пар или ароматерапия. Эти модели дороже, но предлагают больше комфорта и возможностей.
Также существуют комбинированные варианты — душевые кабины с ванной. Они подойдут тем, кто не хочет отказываться от возможности полежать в горячей воде, но при этом ценит удобство душа. Такие модели обычно больше по размеру и требуют просторной ванной комнаты.
Материалы и дизайн
При выборе душевой кабины важно обратить внимание на материалы. Поддоны чаще всего изготавливают из акрила, стали или керамики. Акриловые поддоны легкие, теплые на ощупь и доступны по цене, но могут царапаться. Стальные поддоны прочнее, но холоднее и шумят под струей воды. Керамика долговечна и выглядит эстетично, но она тяжелая и хрупкая.
Стенки и двери обычно делают из закаленного стекла или пластика. Стекло прочнее, выглядит дороже и легче моется, тогда как пластик дешевле, но со временем может помутнеть. Дизайн тоже играет роль: прозрачные, матовые или тонированные стенки позволяют подобрать кабину под стиль интерьера.
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Перед покупкой стоит учесть несколько моментов. Во-первых, размеры. Измерьте пространство в ванной комнате, чтобы кабина не загромождала помещение. Во-вторых, определитесь с функциями: нужны ли вам гидромассаж или парогенератор, или достаточно базовой модели. В-третьих, проверьте качество сборки и репутацию производителя. Надежные бренды обычно предлагают гарантию и запасные части.
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Чтобы душевая кабина служила долго, за ней нужно правильно ухаживать. После каждого использования рекомендуется протирать стенки и поддон мягкой тряпкой, чтобы избежать налета от воды. Для чистки лучше использовать специальные средства для стекла и акрила, избегая агрессивных химикатов. Регулярно проверяйте уплотнители и фурнитуру, чтобы предотвратить протечки.
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Announced in 2016, Gym is an open-source Python library created to facilitate the advancement of support knowing algorithms. It aimed to standardize how environments are specified in AI research study, making released research more easily reproducible [24] [144] while providing users with a simple interface for interacting with these environments. In 2022, new advancements of Gym have been relocated to the library Gymnasium. [145] [146]
Gym Retro
Released in 2018, Gym Retro is a platform for support learning (RL) research study on computer game [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on optimizing representatives to solve single tasks. Gym Retro offers the capability to generalize in between video games with similar concepts however different appearances.
RoboSumo
Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives initially do not have knowledge of how to even walk, but are offered the objectives of discovering to move and to push the opposing agent out of the ring. [148] Through this adversarial learning procedure, the representatives find out how to adjust to changing conditions. When a representative is then eliminated from this virtual environment and positioned in a new virtual environment with high winds, the representative braces to remain upright, recommending it had actually learned how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition between agents could produce an intelligence "arms race" that might increase a representative's capability to operate even outside the context of the competitors. [148]
OpenAI 5
OpenAI Five is a team of 5 OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that learn to play against human players at a high skill level totally through trial-and-error algorithms. Before ending up being a team of 5, the first public presentation occurred at The International 2017, the annual best champion tournament for the video game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for 2 weeks of genuine time, which the learning software was a step in the direction of developing software application that can deal with complicated tasks like a surgeon. [152] [153] The system uses a type of reinforcement knowing, as the bots find out with time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an opponent and taking map objectives. [154] [155] [156]
By June 2018, the capability of the bots broadened to play together as a full group of 5, and they had the ability to beat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against professional players, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champs of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public look came later that month, where they played in 42,729 overall games in a four-day open online competition, winning 99.4% of those video games. [165]
OpenAI 5's mechanisms in Dota 2's bot gamer reveals the challenges of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has shown the usage of deep reinforcement learning (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl
Developed in 2018, Dactyl utilizes maker finding out to train a Shadow Hand, a human-like robot hand, to control physical things. [167] It discovers completely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the things orientation problem by utilizing domain randomization, a simulation technique which exposes the learner to a range of experiences rather than trying to fit to truth. The set-up for Dactyl, aside from having movement tracking cams, likewise has RGB electronic cameras to permit the robotic to manipulate an approximate item by seeing it. In 2018, OpenAI revealed that the system was able to control a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl could resolve a Rubik's Cube. The robot had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to design. OpenAI did this by enhancing the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of creating progressively more tough environments. ADR differs from manual domain randomization by not requiring a human to specify randomization ranges. [169]
API
In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new AI models developed by OpenAI" to let designers contact it for "any English language AI task". [170] [171]
Text generation
The business has popularized generative pretrained transformers (GPT). [172]
OpenAI's initial GPT model ("GPT-1")
The initial paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his colleagues, and published in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a generative model of language could obtain world understanding and process long-range reliances by pre-training on a varied corpus with long stretches of adjoining text.
GPT-2
Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language design and the follower to OpenAI's initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative versions initially released to the public. The full version of GPT-2 was not immediately released due to concern about potential misuse, including applications for composing phony news. [174] Some specialists revealed uncertainty that GPT-2 positioned a significant threat.
In response to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to discover "neural fake news". [175] Other scientists, such as Jeremy Howard, alerted of "the technology to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be impossible to filter". [176] In November 2019, OpenAI released the total version of the GPT-2 language design. [177] Several websites host interactive demonstrations of different circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue without supervision language models to be general-purpose students, illustrated by GPT-2 attaining modern precision and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not additional trained on any task-specific input-output examples).
The corpus it was trained on, called WebText, contains a little 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain concerns encoding vocabulary with word tokens by using byte pair encoding. This permits representing any string of characters by encoding both specific characters and multiple-character tokens. [181]
GPT-3
First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is a not being watched transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI specified that the full version of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 models with as few as 125 million specifications were also trained). [186]
OpenAI mentioned that GPT-3 succeeded at certain "meta-learning" jobs and could generalize the function of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer knowing between English and Romanian, and in between English and German. [184]
GPT-3 considerably improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or experiencing the basic capability constraints of predictive language designs. [187] Pre-training GPT-3 needed a number of thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not right away launched to the general public for issues of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month complimentary private beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was licensed specifically to Microsoft. [190] [191]
Codex
Announced in mid-2021, Codex is a descendant of GPT-3 that has in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the AI powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in personal beta. [194] According to OpenAI, the model can develop working code in over a lots programming languages, most successfully in Python. [192]
Several concerns with glitches, style defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has actually been implicated of producing copyrighted code, with no author attribution or license. [197]
OpenAI revealed that they would terminate support for Codex API on March 23, 2023. [198]
GPT-4
On March 14, 2023, OpenAI revealed the release of Generative Pre-trained Transformer 4 (GPT-4), efficient in accepting text or image inputs. [199] They announced that the upgraded technology passed a simulated law school bar exam with a rating around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also check out, examine or create up to 25,000 words of text, and compose code in all significant programs languages. [200]
Observers reported that the iteration of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained a few of the issues with earlier modifications. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has actually declined to reveal various technical details and data about GPT-4, such as the accurate size of the design. [203]
GPT-4o
On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced results in voice, multilingual, and vision criteria, setting brand-new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) standard compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI launched GPT-4o mini, a smaller variation of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT interface. Its API costs $0.15 per million input tokens and $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI expects it to be particularly beneficial for enterprises, startups and developers seeking to automate services with AI representatives. [208]
o1
On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have been designed to take more time to think about their responses, resulting in higher precision. These models are especially effective in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was changed by o1. [211]
o3
On December 20, 2024, OpenAI revealed o3, the successor of the o1 reasoning design. OpenAI also unveiled o3-mini, a lighter and faster variation of OpenAI o3. As of December 21, 2024, this model is not available for public usage. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the chance to obtain early access to these models. [214] The model is called o3 instead of o2 to avoid confusion with telecoms services company O2. [215]
Deep research
Deep research study is an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 design to carry out substantial web surfing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools allowed, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]
Image category
CLIP
Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to examine the semantic resemblance between text and images. It can notably be used for image category. [217]
Text-to-image
DALL-E
Revealed in 2021, DALL-E is a Transformer design that produces images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to analyze natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of a sad capybara") and create corresponding images. It can produce images of realistic items ("a stained-glass window with an image of a blue strawberry") in addition to things that do not exist in reality ("a cube with the texture of a porcupine"). Since March 2021, no API or code is available.
DALL-E 2
In April 2022, OpenAI revealed DALL-E 2, an upgraded variation of the model with more sensible outcomes. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a new fundamental system for transforming a text description into a 3-dimensional design. [220]
DALL-E 3
In September 2023, OpenAI announced DALL-E 3, a more effective model better able to produce images from intricate descriptions without manual prompt engineering and render complicated details like hands and text. [221] It was launched to the general public as a ChatGPT Plus function in October. [222]
Text-to-video
Sora
Sora is a text-to-video design that can produce videos based on brief detailed prompts [223] in addition to extend existing videos forwards or backwards in time. [224] It can create videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of created videos is unknown.
Sora's development group called it after the Japanese word for "sky", to symbolize its "limitless imaginative potential". [223] Sora's innovation is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos licensed for that purpose, however did not expose the number or the specific sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it might produce videos approximately one minute long. It also shared a technical report highlighting the approaches used to train the design, and the model's capabilities. [225] It acknowledged some of its drawbacks, consisting of struggles imitating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", but noted that they should have been cherry-picked and may not represent Sora's normal output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, significant entertainment-industry figures have actually shown substantial interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the technology's capability to generate reasonable video from text descriptions, citing its prospective to reinvent storytelling and material production. He said that his excitement about Sora's possibilities was so strong that he had chosen to pause plans for expanding his Atlanta-based motion picture studio. [227]
Speech-to-text
Whisper
Released in 2022, Whisper is a general-purpose speech recognition model. [228] It is trained on a large dataset of diverse audio and is likewise a multi-task design that can perform multilingual speech acknowledgment along with speech translation and language recognition. [229]
Music generation
MuseNet
Released in 2019, MuseNet is a deep neural net trained to anticipate subsequent musical notes in MIDI music files. It can create songs with 10 instruments in 15 designs. According to The Verge, a song produced by MuseNet tends to begin fairly however then fall under mayhem the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were utilized as early as 2020 for the web mental thriller Ben Drowned to produce music for the titular character. [232] [233]
Jukebox
Released in 2020, Jukebox is an open-sourced algorithm to produce music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a bit of lyrics and outputs tune samples. OpenAI specified the tunes "reveal local musical coherence [and] follow standard chord patterns" but acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that duplicate" which "there is a substantial space" between Jukebox and human-generated music. The Verge specified "It's technically outstanding, even if the results sound like mushy variations of songs that might feel familiar", while Business Insider specified "surprisingly, some of the resulting tunes are appealing and sound genuine". [234] [235] [236]
User interfaces
Debate Game
In 2018, OpenAI released the Debate Game, which teaches machines to dispute toy problems in front of a human judge. The function is to research study whether such a method might assist in auditing AI decisions and in developing explainable AI. [237] [238]
Microscope
Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and neuron of eight neural network designs which are typically studied in interpretability. [240] Microscope was developed to analyze the functions that form inside these neural networks easily. The designs included are AlexNet, VGG-19, different versions of Inception, and various variations of CLIP Resnet. [241]
ChatGPT
Launched in November 2022, ChatGPT is an expert system tool constructed on top of GPT-3 that offers a conversational interface that enables users to ask concerns in natural language. The system then responds with an answer within seconds.
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