/**
* Transparent Header - Customizer.
*
* @package Astra
* @since 1.0.0
*/
if ( ! defined( 'ABSPATH' ) ) {
exit; // Exit if accessed directly.
}
if ( ! class_exists( 'Astra_Ext_Transparent_Header_Loader' ) ) {
/**
* Customizer Initialization
*
* @since 1.0.0
*/
class Astra_Ext_Transparent_Header_Loader {
/**
* Member Variable
*
* @var object instance
*/
private static $instance;
/**
* Initiator
*/
public static function get_instance() {
if ( ! isset( self::$instance ) ) {
self::$instance = new self();
}
return self::$instance;
}
/**
* Constructor
*/
public function __construct() {
add_filter( 'astra_theme_defaults', array( $this, 'theme_defaults' ) );
add_action( 'customize_preview_init', array( $this, 'preview_scripts' ) );
add_action( 'customize_register', array( $this, 'customize_register' ), 2 );
}
/**
* Set Options Default Values
*
* @param array $defaults Astra options default value array.
* @return array
*/
public function theme_defaults( $defaults ) {
// Header - Transparent.
$defaults['transparent-header-logo'] = '';
$defaults['transparent-header-retina-logo'] = '';
$defaults['different-transparent-logo'] = 0;
$defaults['different-transparent-retina-logo'] = 0;
$defaults['transparent-header-logo-width'] = array(
'desktop' => 150,
'tablet' => 120,
'mobile' => 100,
);
$defaults['transparent-header-enable'] = 0;
/**
* Old option for 404, search and archive pages.
*
* For default value on separate option this setting is in use.
*/
$defaults['transparent-header-disable-archive'] = 1;
$defaults['transparent-header-disable-latest-posts-index'] = 1;
$defaults['transparent-header-on-devices'] = 'both';
$defaults['transparent-header-main-sep'] = '';
$defaults['transparent-header-main-sep-color'] = '';
/**
* Transparent Header
*/
$defaults['transparent-header-bg-color'] = '';
$defaults['transparent-header-color-site-title'] = '';
$defaults['transparent-header-color-h-site-title'] = '';
$defaults['transparent-menu-bg-color'] = '';
$defaults['transparent-menu-color'] = '';
$defaults['transparent-menu-h-color'] = '';
$defaults['transparent-submenu-bg-color'] = '';
$defaults['transparent-submenu-color'] = '';
$defaults['transparent-submenu-h-color'] = '';
$defaults['transparent-header-logo-color'] = '';
/**
* Transparent Header Responsive Colors
*/
$defaults['transparent-header-bg-color-responsive'] = array(
'desktop' => '',
'tablet' => '',
'mobile' => '',
);
$defaults['hba-transparent-header-bg-color-responsive'] = array(
'desktop' => '',
'tablet' => '',
'mobile' => '',
);
$defaults['hbb-transparent-header-bg-color-responsive'] = array(
'desktop' => '',
'tablet' => '',
'mobile' => '',
);
$defaults['transparent-header-color-site-title-responsive'] = array(
'desktop' => '',
'tablet' => '',
'mobile' => '',
);
$defaults['transparent-header-color-h-site-title-responsive'] = array(
'desktop' => '',
'tablet' => '',
'mobile' => '',
);
$defaults['transparent-menu-bg-color-responsive'] = array(
'desktop' => '',
'tablet' => '',
'mobile' => '',
);
$defaults['transparent-menu-color-responsive'] = array(
'desktop' => '',
'tablet' => '',
'mobile' => '',
);
$defaults['transparent-menu-h-color-responsive'] = array(
'desktop' => '',
'tablet' => '',
'mobile' => '',
);
$defaults['transparent-submenu-bg-color-responsive'] = array(
'desktop' => '',
'tablet' => '',
'mobile' => '',
);
$defaults['transparent-submenu-color-responsive'] = array(
'desktop' => '',
'tablet' => '',
'mobile' => '',
);
$defaults['transparent-submenu-h-color-responsive'] = array(
'desktop' => '',
'tablet' => '',
'mobile' => '',
);
$defaults['transparent-content-section-text-color-responsive'] = array(
'desktop' => '',
'tablet' => '',
'mobile' => '',
);
$defaults['transparent-content-section-link-color-responsive'] = array(
'desktop' => '',
'tablet' => '',
'mobile' => '',
);
$defaults['transparent-content-section-link-h-color-responsive'] = array(
'desktop' => '',
'tablet' => '',
'mobile' => '',
);
return $defaults;
}
/**
* Add postMessage support for site title and description for the Theme Customizer.
*
* @param WP_Customize_Manager $wp_customize Theme Customizer object.
*/
public function customize_register( $wp_customize ) {
// @codingStandardsIgnoreStart WPThemeReview.CoreFunctionality.FileInclude.FileIncludeFound
/**
* Register Panel & Sections
*/
require_once ASTRA_THEME_TRANSPARENT_HEADER_DIR . 'classes/class-astra-transparent-header-panels-and-sections.php';
/**
* Sections
*/
require_once ASTRA_THEME_TRANSPARENT_HEADER_DIR . 'classes/sections/class-astra-customizer-colors-transparent-header-configs.php';
// Check Transparent Header is activated.
require_once ASTRA_THEME_TRANSPARENT_HEADER_DIR . 'classes/sections/class-astra-customizer-transparent-header-configs.php';
// @codingStandardsIgnoreEnd WPThemeReview.CoreFunctionality.FileInclude.FileIncludeFound
}
/**
* Customizer Preview
*/
public function preview_scripts() {
/**
* Load unminified if SCRIPT_DEBUG is true.
*/
/* Directory and Extension */
$dir_name = ( SCRIPT_DEBUG ) ? 'unminified' : 'minified';
$file_prefix = ( SCRIPT_DEBUG ) ? '' : '.min';
wp_enqueue_script( 'astra-transparent-header-customizer-preview-js', ASTRA_THEME_TRANSPARENT_HEADER_URI . 'assets/js/' . $dir_name . '/customizer-preview' . $file_prefix . '.js', array( 'customize-preview', 'astra-customizer-preview-js' ), ASTRA_THEME_VERSION, true );
// Localize variables for further JS.
wp_localize_script(
'astra-transparent-header-customizer-preview-js',
'AstraBuilderTransparentData',
array(
'is_astra_hf_builder_active' => Astra_Builder_Helper::$is_header_footer_builder_active,
'is_flex_based_css' => Astra_Builder_Helper::apply_flex_based_css(),
'transparent_header_devices' => astra_get_option( 'transparent-header-on-devices' ),
)
);
}
}
}
/**
* Kicking this off by calling 'get_instance()' method
*/
Astra_Ext_Transparent_Header_Loader::get_instance();
ChatGPT 5 release date: what we know about OpenAIs next chatbot as rumours suggest summer release
Yes, GPT-5 is coming at some point in the future although a firm release date hasn’t been disclosed yet. CISOs know that getting board buy-in starts with a clear, strategic view of how cloud security drives business value. Ahead of GPT-5 debut, OpenAI announced ChatGPT Agent, which can think and act, proactively choose from a toolbox of agentic skills to complete tasks for you using its own computer.
Free users can also use ChatGPT’s web-browsing tool and memory features and can upload photos and files for the chatbot to analyze. According to the Business Insider report, some businesses that have the pricey ChatGPT Enterprise paid plan already have an early access to beta versions of GPT-5. Enterprise prices aren’t public, but some reports put the cost at around $60 per user per month with a 150-seat minimum. Barret Zoph, a research lead at OpenAI, was recently demonstrating the new GPT-4o model and its ability to detect human emotions though a smartphone camera when ChatGPT misidentified his face as a wooden table.
I, for one, love learning about new things on a regular basis, so I can’t wait to give this new tool a try. In February, OpenAI boss Sam Altman spoke to Bill Gates about what we can expect from the upgraded version of ChatGPT. According to OpenAI CEO Sam Altman, GPT-5 will introduce support for new multimodal input such as video as well as broader logical reasoning abilities.
In a January 2024 interview with Bill Gates, Altman confirmed that development on GPT-5 was underway. He also said that OpenAI would focus on building better reasoning capabilities as well as the ability to process videos. The current-gen GPT-4 model already offers speech and image functionality, so video is the next logical step.
After a quick laugh, Zoph assured GPT-4o that he’s not a table and asked the AI tool to take a fresh look at the app’s live video rather than a photo he shared earlier. “Ah, that makes more sense,” said ChatGPT’s AI voice, before describing his facial expression and potential emotions. GPT-4’s impressive skillset and ability to mimic humans sparked fear in the tech community, prompting many to question the ethics and legality of it all. Some notable personalities, including Elon Musk and Steve Wozniak, have warned about the dangers of AI and called for a unilateral pause on training models “more advanced than GPT-4”. At the time of this writing, there are several newer models that have followed the initial launch of GPT-4 including GPT-4o, o3, o4, and several other similar variants. It’s been years now since ChatGPT first blew us away with its impressive natural language capabilities.
This week, Google will go head-to-head with ChatGPT while introducing its own AI model, which is expected to exceed ChatGPT features in terms of accuracy and speed. The event will showcase how Google’s AI model surpasses ChatGPT’s search engine capabilities with features such as more accurate search results and faster loading times. It has become popular due to its user-friendly search options and alternative search results. In addition to limited GPT-4o access, nonpaying users received a major upgrade to their overall user experience, with multiple features that were previously just for paying customers. The GPT Store, where anyone can release a version of ChatGPT with custom instructions, is now widely available.
Last month, Sam Altman hinted at GPT-5 arriving during the Western Hemisphere’s Summer months, and «Study together» could be part of that launch. Hinting at its smarts, the OpenAI boss told the FT that GPT-5 would require more data to train on. The plan, he said, was to use publicly available data sets from the internet, along with large-scale proprietary data sets from organisations. Altman said that the next ChatGPT still fell short of artificial general intelligence, according to Masood and Shams.
During the presentation, he also showed how the voice mode could be used to translate between English and Italian. After the presentation, the company released another video showing speech translation working in real time. GPT stands for generative pre-trained transformer, which is a type of large language model that can create human-like text and content such as images. At the time, in mid-2023, OpenAI announced that it had no intentions of training a successor to GPT-4. However, that changed by the end of 2023 following a long-drawn battle between CEO Sam Altman and the board over differences in opinion. Altman reportedly pushed for aggressive language model development, while the board had reservations about AI safety.
ChatGPT-5, the next iteration of OpenAI’s language model, is reportedly set to be released this summer. The new model may be smarter either because of better contextual responses or increased training data. It might be multimodal, meaning it could handle generating other media in addition to text — GPT-4 is partially multimodal, as it can process images and audio. While GPT-4 came just shortly after GPT-3 became public, the road to GPT-5 has been much longer as the company experimented with reasoning models and several other variants first. The good news is that GPT-5 is expected this year, though there’s been no clear date on when it will arrive. In addition to GPT-5, OpenAI plans to upgrade Operator and is adding new features to Image Gen model, including a new toggle to select styles.
]]>The reason I’ve chosen to focus on this technology instead of something like, say, AI for math-based analysis, is the increasingly large application for NLP. Wit.ai is already free and doesn’t place any limits on requests. Most of the other natural language APIs are free for tinkerers at the start and only start charging after a predetermined number of requests.
If you had a billion dollar idea to revolutionize conversational AI, you would probably want to hire some PhDs and build your product from scratch. But for everyone else, natural language APIs are more than sufficient for pulling structured data out of human language. Today, I’m touching on something called natural language processing (NLP). It’s a form of artificial intelligence that focuses on analyzing the human language to draw insights, create advertisements, help you text (yes, really) and more.
If the HMM method breaks down text and NLP allows for human-to-computer communication, then semantic analysis allows everything to make sense contextually. Semantic analysis is how NLP AI interprets human sentences logically. When the HMM method breaks sentences down into their basic structure, semantic analysis helps the process add content. Next is the actual understanding of the language and context. Each NLP system uses slightly different techniques, but on the whole, they’re fairly similar. The systems try to break each word down into its part of speech (noun, verb, etc.).
Let’s use an example to show just how powerful NLP is when used in a practical situation. When you’re typing on an iPhone, like many of us do every day, you’ll see word suggestions based on what you type and what you’re currently typing. NLP is an emerging technology that drives many forms of AI you’re used to seeing.
It’s no surprise then that businesses of all sizes are taking note of large companies’ success with AI and jumping on board. Some forms of artificial intelligence are more useful than others. The first step in NLP depends on the application of the system. Voice-based systems like Alexa or Google Assistant need to translate your words into text.
I probably got more pitches for bot startups than anything else. If we hope to break beyond the rigid functionality of today’s tools, a prerequisite is going to be giving bot developers a bit more open source love. The HMM uses math models to determine what you’ve said and translate that into text usable by the NLP system. Google, Netflix, data companies, video games and more all use AI to comb through large amounts of data.
As mentioned above, natural language processing is a form of artificial intelligence that analyzes the human language. It takes many forms, but at its core, the technology helps machine understand, and even communicate with, human speech. For folks who don’t spend a lot of time with engineers, APIs allow developers to rapidly create products without having to reinvent the wheel. Natural language processing, i.e converting human language into something a computer can understand, is pretty difficult but incredibly necessary for creating bots.
The end result is insights and analysis that would otherwise either be impossible or take far too long. Companies large and small are building on top of RASA, in-part because it’s customizable by nature. Developers can tune their models to make them fit specific use cases. Ultimately, this enables the creation of better final products. The next few years should see AI technology increase even more, with the global AI market expected to push $60 billion by 2025 (registration required). It’s a very advanced form of AI that’s only recently become viable.
That’s done (usually) using the Hidden Markov Models system (HMM). The team agrees that right now we are struggling to find good use cases for bots. It doesn’t take a genius to realize that even the best conversational AIs available today are little more than glorified voice-activated remote controls.
But, if you’re looking to push a bot to market you probably want more ownership over your product. I might not touch on every technical definition, but what follows is the easiest way to understand how natural language processing works. Every day, humans say thousands of words that other humans interpret to do countless things.
At its core, it’s simple communication, but we all know words run much deeper than that. There’s a context that we derive from everything someone says. Whether they imply something with their body language or in how often they mention something. While NLP doesn’t focus on voice inflection, it does draw on contextual patterns.
]]>Natural Language Understanding (NLU) can be considered the process of understanding and extracting meaning from human language. It is a subset ofNatural Language Processing (NLP), which also encompasses syntactic and pragmatic analysis, as well as discourse processing. In human language processing, NLP and NLU, while visually resembling each other, serve distinct functions. Examining “NLU vs NLP” reveals key differences in four crucial areas, highlighting the nuanced disparities between these technologies in language interpretation.
NLP primarily focuses on surface-level aspects such as sentence structure, word order, and basic syntax. However, its emphasis is limited to language processing and manipulation without delving deeply into the underlying semantic layers of text or voice data. NLP excels in tasks related to the structural aspects of language but doesn’t extend its reach to a profound understanding of the nuanced meanings or semantics within the content.
NLP provides the foundation for NLU by extracting structural information from text or speech, while NLU enriches NLP by inferring meaning, context, and intentions. This collaboration enables machines to not only process and generate human-like language but also understand and respond intelligently to user inputs. Natural Language Generation (NLG) is an essential component of Natural Language Processing (NLP) that complements the capabilities of natural language understanding. While NLU focuses on interpreting human language, NLG takes structured and unstructured data and generates human-like language in response. As the name suggests, the initial goal of NLP is language processing and manipulation. It focuses on the interactions between computers and individuals, with the goal of enabling machines to understand, interpret, and generate natural language.
It’s a subset of artificial intelligence and has many applications, such as speech recognition, translation and sentiment analysis. As a result, algorithms search for associations and correlations to infer what the sentence’s most likely meaning is rather than understanding the genuine meaning of human languages. It enables computers to evaluate and organize unstructured text or speech input in a meaningful way that is equivalent to both spoken and written human language. NLU delves into comprehensive analysis and deep semantic understanding to grasp the meaning, purpose, and context of text or voice data. NLU techniques enable systems to tackle ambiguities, capture subtleties, recognize linkages, and interpret references within the content.
Extractive summarization is the AI innovation powering Key Point Analysis used in That’s Debatable. Using symbolic AI, everything is visible, understandable and explained within a transparent box that delivers complete insight into how the logic was derived. This transparency makes symbolic AI an appealing choice for those who want the flexibility to change difference between nlp and nlu the rules in their NLP model. This is especially important for model longevity and reusability so that you can adapt your model as data is added or other conditions change. In the world of AI, for a machine to be considered intelligent, it must pass the Turing Test. A test developed by Alan Turing in the 1950s, which pits humans against the machine.
Breaking Down 3 Types of Healthcare Natural Language Processing.
Posted: Wed, 20 Sep 2023 07:00:00 GMT [source]
For example, programming languages including C, Java, Python, and many more were created for a specific reason. Latin, English, Spanish, and many other spoken languages are all languages that evolved naturally over time. NLU, however, understands the idiom and interprets the user’s intent as being hungry and searching for a nearby restaurant. Two fundamental concepts of NLU are intent recognition and entity recognition. In conclusion, I hope now you have a better understanding of the key differences between NLU and NLP.
]]>Automation dramatically improves operational efficiency and cuts customer service costs. It significantly eliminates repetitive tasks, instantly resolves frequent simple requests, allowing your support agents to handle more complex inquiries in less time. When it comes to phone systems, you can’t go wrong with Nextiva’s customer service tools. It eliminates busywork and lets your team serve customers across many channels without distractions.
Moreover, AI customer service software is able to identify which visitors are most likely to make a purchase. When powered by artificial intelligence (AI), automation technology is extremely effective at handling a help desk’s most repetitive tasks without any human interaction. In fact, experts predict that AI will be able to automate 95% of customer interactions by 2025.
The thing is, automated systems may only sometimes give accurate or detailed answers to tricky customer questions, leaving folks feeling like they need to be in touch with the customer service team. Through automated customer service, agents can escape the monotony of routine tasks and embrace their role as problem solvers and brand ambassadors. This allows them to utilize their expertise, critical thinking abilities, and empathy to provide personalized support and build stronger customer relationships. Customer service automation is the process of using technology to carry out certain aspects of customer support. Automated technology helps companies respond proactively to simple inquiries, manage data, and provide self-service options.
When customer service automation is implemented, the goal is to leverage software and tech innovation to provide human-less and prompt support on a 24×7 basis. The use of customer service chatbots ensures instant replies to customers while agents save effort and time that would otherwise go in handling queries. One of the most popular automated advantages of automated customer service customer service examples are chatbots. They offer quick and accurate solutions to customer queries, lightening the load on your hardworking human agents. For example, automation technology can help support teams by providing contextual article recommendations based on customer feedback and automatically routing requests to the right agents.
This helps boost agent productivity and allows agents to focus on resolving issues that truly require a human touch. The second component to live chat is to offer an automated chatbot to handle inquiries outside of business hours. After you perfect the right responses, chatbots can function as standalone virtual agents on your website.It can be scary to entrust your entire customer experience in the hands of a bot. Never let automation distract attention from your focus on delighting the customer. Use data accumulated by chatbots to improve your customer service skills and team’s familiarity with customer concerns and people skills. Additionally, automating customer service streamlines various processes, such as data entry and ticket routing, resulting in faster handling of customer interactions.

It’s not a case of replacing your existing staff but rather complementing them so they can deal with more complex cases. It also saves you from diverting staff to train new team members, which can also be costly. You will already know that certain queries are submitted time and time again.
]]>