Customer support is a range of services to assist customers in making cost effective and correct use of a product. It includes assistance in planning, installation, training, troubleshooting, maintenance, upgrading, and disposal of a product. Regarding technology products such as mobile phones, televisions, computers, software products or other electronic or mechanical goods, it is termed technical support. It aims to ensure users can effectively operate the product and resolve any issues that may arise throughout its lifecycle. Support is delivered through various channels, including telephone, email, live chat, self-service knowledge bases, and social media. Research indicates that most customers attempt to resolve issues through self-service before contacting a representative. For products sold across multiple regions, support may be provided in several languages, as consumers tend to prefer assistance in their native language. Requirements for customer contact centres are defined in international standards such as ISO 18295.
AI Mode
AI Mode is a search feature used within Google Search. In March 2025, Google introduced an experimental "AI Mode" within its search platform, enabling users to input complex, multi-part queries and receive comprehensive, AI-generated responses. This feature uses Google's Gemini model, which enhances the system's reasoning capabilities and supports multimodal inputs, including text, images, and voice. Users need to be signed in to be able to use the image generation features. Initially, AI Mode was available to Google One AI Premium subscribers in the United States, who could access it through the Search Labs platform. This phased rollout allowed Google to gather user feedback and refine the feature before a broader release.
Huawei Mobile Services
Huawei Mobile Services (HMS) is a collection of proprietary services and high level application programming interfaces (APIs) developed by Huawei Technologies Co., Ltd. Its hub known as HMS Core serves as a toolkit for app development on Huawei devices. HMS is typically installed on Huawei devices on top of running HarmonyOS 4.x and earlier operating system on its earlier devices running the Android operating system with EMUI including devices already distributed with Google Mobile Services. Alongside, HMS Core Wear Engine for Android phones with lightweight based LiteOS wearable middleware app framework integration connectivity like notifications, status etc. HMS consists of seven key services and the HMS Core. The key services are Huawei ID, Huawei Cloud, AppGallery, Themes, Huawei Video, Browser, and Assistant. The web browser is based on Chromium. Huawei Quick Apps is the alternative to Google Instant Apps. By January 2020, over 50,000 apps had been integrated with HMS Core. Its rival, Google Mobile Services has 3 million apps on Google's Play Store. The AppGallery claimed 180 billion downloads in 2019. In March 2020, HMS was used by 650 million monthly active users across 170 countries. A Chinese phone manufacturer, LeTV, hosted a smartphone business communication meeting in Beijing on September 27, 2021, to demonstrate its phone, the LeTV S1. This was the first smartphone from a third-party manufacturer to include Huawei Mobile Services (HMS). == HMS on Android and HarmonyOS == Huawei Mobile Services on Android goes all the way back to August 2016 as Huawei ID services for phones, basic functionalities for Huawei P9 series. However, in May 2019 proved to be a significant change to HMS when Google was prohibited from working with Huawei on any new devices extending ecosystem for AppGallery store front launched in April 2018, year prior. This also included bundling Google's Apps, including Gmail, Maps and YouTube. Any new Huawei devices launched after 16 May 2019 were unable to receive updates from Google services and would be considered 'uncertified' meaning Huawei's only solution at the time was to turn HMS into a genuine competitor to Google and incentivize app developers to utilize the platform. Huawei officially launched Huawei Mobile Services in China on December 24, 2019, as a beta. Huawei expanded Huawei Mobile Services in Europe in February 2020 and other markets in Asia, Latin America, Middle East & Africa, Canada, Mexico followed outside banned US market. HMS is available on the Honor 9X Pro, View 30 Pro, Huawei Mate XS. HMS is also available, alongside GMS, on many other Huawei models launched before the ban. Huawei promised developers it would take, “less than 10 minutes", to port their app over to HMS - to illustrate the ease of portability between Google's Play Store and the HMS AppGallery. On January 15, 2020, HMS Core 4.0 (Huawei Mobile Services Core 4.0) was officially launched. Huawei announced that at this time, there were already 1.3 million developers and 55,000 applications on board. The next day, Huawei held a developer day event in London and invested £20 million to encourage developers in the United Kingdom and Ireland to use HMS. On July 15, 2021, Huawei expanded HMS with classic HarmonyOS dual-framework that provided Java support and eventually with JavaScript and ArkTS (eTS) language support with HMS Core 6.0 for app development with primarily Android apps, alongside limited HAP imperative developed based apps that shares AOSP file system libraries in all types of devices from smartphones, tablets, smart screens, smartwatches, and car machines. Including various third-party development frameworks, such as React Native, Cordova, etc. At HDC 2023, Huawei unveiled HarmonyOS 5, marking a total break from the hybrid Android derived platform. This shift replaced the legacy Android and classic HarmonyOS-based HMS SDK with a full native API developer kit SDK built solely on OpenHarmony. The architecture moved from middleware services to vertical integration path. In this new model, HMS Core libraries are no longer external add-ons but are bundled directly into the system and DevEco Studio as native HarmonyOS Kits. == HMS Core == HMS Core is a hub for Huawei Mobile Services and serves as a toolkit for app development on Huawei devices. The core comprises Development, Growth and Monetizing and was created as a replacement for Google Mobile Services (GMS) Core. HMS core services were available in more than 55,000 apps in June 2020; HMS Core 5.0 debuted in September 2020. HMS Core 6.0 was launched in June 2021 with extended support for Huawei Cloud services. In June 2021, the number of registered developers within the HMS ecosystem was 4 million, and the number of apps integrated with the HMS Core had reached 134,000. As of July 2022, registered developers within HMS ecosystem had grown to 5 million, and the number of apps integrated with the HMS Core reached 203,000. The number of apps had grown to 220,000 by 30 September 2022. == AppGallery == The AppGallery has a key rival, Google's Play Store on Android. The AppGallery is available in 170 countries, across 78 languages. == Reception == The reception of HMS is mixed, with the majority of discussion based around the key Google/Android apps which are not yet present on the AppGallery and whether or not this presents a significant problem to users. The open development of HMS Core has been regarded by some as benefiting the Android project as a whole, "If Huawei continues to invest in a holistically open approach ... the result could be that we could all end up a bit less beholden to Google".
Convolutional layer
In artificial neural networks, a convolutional layer is a type of network layer that applies a convolution operation to the input. Convolutional layers are some of the primary building blocks of convolutional neural networks (CNNs), a class of neural network most commonly applied to images, video, audio, and other data that have the property of uniform translational symmetry. The convolution operation in a convolutional layer involves sliding a small window (called a kernel or filter) across the input data and computing the dot product between the values in the kernel and the input at each position. This process creates a feature map that represents detected features in the input. == Concepts == === Kernel === Kernels, also known as filters, are small matrices of weights that are learned during the training process. Each kernel is responsible for detecting a specific feature in the input data. The size of the kernel is a hyperparameter that affects the network's behavior. === Convolution === For a 2D input x {\displaystyle x} and a 2D kernel w {\displaystyle w} , the 2D convolution operation can be expressed as: y [ i , j ] = ∑ m = 0 k h − 1 ∑ n = 0 k w − 1 x [ i + m , j + n ] ⋅ w [ m , n ] {\displaystyle y[i,j]=\sum _{m=0}^{k_{h}-1}\sum _{n=0}^{k_{w}-1}x[i+m,j+n]\cdot w[m,n]} where k h {\displaystyle k_{h}} and k w {\displaystyle k_{w}} are the height and width of the kernel, respectively. This generalizes immediately to nD convolutions. Commonly used convolutions are 1D (for audio and text), 2D (for images), and 3D (for spatial objects, and videos). === Stride === Stride determines how the kernel moves across the input data. A stride of 1 means the kernel shifts by one pixel at a time, while a larger stride (e.g., 2 or 3) results in less overlap between convolutions and produces smaller output feature maps. === Padding === Padding involves adding extra pixels around the edges of the input data. It serves two main purposes: Preserving spatial dimensions: Without padding, each convolution reduces the size of the feature map. Handling border pixels: Padding ensures that border pixels are given equal importance in the convolution process. Common padding strategies include: No padding/valid padding. This strategy typically causes the output to shrink. Same padding: Any method that ensures the output size same as input size is a same padding strategy. Full padding: Any method that ensures each input entry is convolved over for the same number of times is a full padding strategy. Common padding algorithms include: Zero padding: Add zero entries to the borders of input. Mirror/reflect/symmetric padding: Reflect the input array on the border. Circular padding: Cycle the input array back to the opposite border, like a torus. The exact numbers used in convolutions is complicated, for which we refer to (Dumoulin and Visin, 2018) for details. == Variants == === Standard === The basic form of convolution as described above, where each kernel is applied to the entire input volume. === Depthwise separable === Depthwise separable convolution separates the standard convolution into two steps: depthwise convolution and pointwise convolution. The depthwise separable convolution decomposes a single standard convolution into two convolutions: a depthwise convolution that filters each input channel independently and a pointwise convolution ( 1 × 1 {\displaystyle 1\times 1} convolution) that combines the outputs of the depthwise convolution. This factorization significantly reduces computational cost. It was first developed by Laurent Sifre during an internship at Google Brain in 2013 as an architectural variation on AlexNet to improve convergence speed and model size. === Dilated === Dilated convolution, or atrous convolution, introduces gaps between kernel elements, allowing the network to capture a larger receptive field without increasing the kernel size. === Transposed === Transposed convolution, also known as deconvolution, fractionally strided convolution, and upsampling convolution, is a convolution where the output tensor is larger than its input tensor. It's often used in encoder-decoder architectures for upsampling. It's used in image generation, semantic segmentation, and super-resolution tasks. == History == The concept of convolution in neural networks was inspired by the visual cortex in biological brains. Early work by Hubel and Wiesel in the 1960s on the cat's visual system laid the groundwork for artificial convolution networks. An early convolution neural network was developed by Kunihiko Fukushima in 1969. It had mostly hand-designed kernels inspired by convolutions in mammalian vision. In 1979 he improved it to the Neocognitron, which learns all convolutional kernels by unsupervised learning (in his terminology, "self-organized by 'learning without a teacher'"). During the 1988 to 1998 period, a series of CNN were introduced by Yann LeCun et al., ending with LeNet-5 in 1998. It was an early influential CNN architecture for handwritten digit recognition, trained on the MNIST dataset, and was used in ATM. (Olshausen & Field, 1996) discovered that simple cells in the mammalian primary visual cortex implement localized, oriented, bandpass receptive fields, which could be recreated by fitting sparse linear codes for natural scenes. This was later found to also occur in the lowest-level kernels of trained CNNs. The field saw a resurgence in the 2010s with the development of deeper architectures and the availability of large datasets and powerful GPUs. AlexNet, developed by Alex Krizhevsky et al. in 2012, was a catalytic event in modern deep learning. In that year’s ImageNet competition, the AlexNet model achieved a 16% top-five error rate, significantly outperforming the next best entry, which had a 26% error rate. The network used eight trainable layers, approximately 650,000 neurons, and around 60 million parameters, highlighting the impact of deeper architectures and GPU acceleration on image recognition performance. From the 2013 ImageNet competition, most entries adopted deep convolutional neural networks, building on the success of AlexNet. Over the following years, performance steadily improved, with the top-five error rate falling from 16% in 2012 and 12% in 2013 to below 3% by 2017, as networks grew increasingly deep.
Bottlenose (company)
Bottlenose.com, also known as Bottlenose, is an enterprise trend intelligence company that analyzes big data and business data to detect trends for brands. It helps Fortune 500 enterprises discover, and track emerging trends that affect their brands. The company uses natural language processing, sentiment analysis, statistical algorithms, data mining, and machine learning heuristics to determine trends, and has a search engine that gathers information from social networks. KPMG Capital has invested a "substantial amount" in the company. Bottlenose processed 72 billion messages per day, in real-time, from across social and broadcast media, as of December 2014. == History == The company is based in Los Angeles, CA. Bottlenose is a real-time trend intelligence tool that measures social media campaigns and trends. The company also provides a free version of its Sonar tool that shows real-time trends across social media. In October 2012, the company received $1 million of funding from ff Venture Capital and Prosper Capital. By 2014, the company raised about $7 million in funding. In December 2014, KPMG Capital announced further investment in the company. In February 2015, the company confirmed it had raised $13.4 million in Series B funding led by KPMG Capital. Bottlenose partnered with the nonprofit No Labels during the 2014 State of the Union Address to analyze Twitter conversations for bipartisanship. The company also partnered with media monitoring company Critical Mention to analyze broadcast analytics. The Bottlenose Nerve Center integrated with the Critical Mention API to analyze real-time trends in television and radio broadcasts. In June 2014, Bottlenose updated its trend detection product to Nerve Center 2.0. It creates a newsfeed to show changes in trends and sends alerts when trends occur. It also has "emotion detection," which will display the emotions associated with specific comments on trending topics. In 2016, Bottlenose released its Nerve Center 3.0 platform, which was designed to automate the work of data scientists and lower the cost of artificial intelligence for businesses.
Conversational user interface
A conversational user interface (CUI) is a user interface for computers that emulates a conversation with a human. Historically, computers have relied on text-based user interfaces and graphical user interfaces (GUIs) (such as the user pressing a "back" button) to translate the user's desired action into commands the computer understands. While an effective mechanism of completing computing actions, there is a learning curve for the user associated with GUI. Instead, CUIs provide opportunity for the user to communicate with the computer in their natural language rather than in a syntax specific commands.
Jais (language model)
Jais is an open-source large language model launched in August 2023. Developed as a collaboration between Emirati AI company G42, the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), and US-based Cerebras Systems, Jais was designed to produce high-quality Arabic text and was also trained on English data. The model's creation was motivated by the underrepresentation of the Arabic language in the field of generative artificial intelligence. It aims to provide a more culturally and linguistically accurate model for the world's 400 million Arabic speakers. Its name is a reference to Jebel Jais, the highest mountain in the UAE. == Background and development == Jais was developed in response to the limited availability of advanced generative artificial intelligence models for the Arabic language, despite it being spoken by over 400 million people. Existing models were often trained on limited or low-quality Arabic web content, resulting in poor performance. The project represents a significant investment by the United Arab Emirates in the field of AI as part of its national strategy. The model was created through a partnership between Inception (now Core42), a subsidiary of the Abu Dhabi-based AI company G42; the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI); and Cerebras Systems, a US company specializing in AI hardware. The model is named after Jebel Jais, the highest peak in the UAE. == Training == The initial version of Jais released in August 2023 had 13 billion parameters. In November 2023, Core42 released Jais 30B, an improved version with 30 billion parameters. Both models were trained on a subset of the Cerebras Condor Galaxy 1 supercomputer. The training dataset consisted of a mix of Arabic, English, and computer code. According to Timothy Baldwin, a professor of natural language processing at MBZUAI, training the model on a diverse Arabic dataset allows it to switch between dialects. == Features == Jais is designed to generate text in both English and Arabic. The project has also released instruction-tuned "Chat" variants for both the 13B and 30B models, which are specifically optimized for conversational applications. Additional functionality for working with images, graphs, and tabular data is planned for future releases.