Get the most recent info and news about Synthesia on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

Create professional AI videos from text in 60+ languages. Synthesia saves you money, time and quite some nerves. Perfect for building training and instructional videos 👉 Trusted by Accenture, Nike, BBC, Google, Amazon and more.

Synthesia

synthesia

AI firm <strong>Synthesia</strong> has unveiled AI-generated digital avatars capable of mimicking human emotions, which could transform commerce.

Emotive AI Offers New Ways to Connect With Consumers

<strong>Synthesia</strong> introduced its fourth-generation &quot;Expressive Avatars,&quot; which it said can express emotions like sadness and excitement based on context

Nvidia-backed startup Synthesia's AI avatars can express human emotions

In the recent past with the genesis of large language models, one thing that we critically debate around is whether like AI art, AI videos, etc will destroy the creativity of content creators or can enhance the quality and assist them. In my , I talked about how AI code-generation tools can help add value to the software development cycle, in this post I will mainly be highlighting how by using AI tools creating video content is not only getting easy and fast but also creative. AI-generated content previous blog Before understanding how one can leverage current video and image models in generating creative and engaging content, it is important to understand what the current state looks like, we can layer the current AI video generation landscape as below: into their workflow like Adobe, Canva, etc. Existing video editors trying to integrate AI like Fliki.ai, unscreen.com, synthesia.ai, hourone.ai, etc. AI-based new-age video editing tools dedicated to single use case in video generation workflow, eg helps in creating realistic images for videos, provides a platform to convert image to video or image to image, helps in adding animation to image, and so on. Abstraction layer Midjourney RunwayML Did which forms the base of the entire landscape, software teams can leverage this layer to customize for their use cases. Model Layer As we move above in layer flexibility to customize decreases while ease of use increases, for the context of this article we will be diving deep to understand how we can use the in generating creative videos since this layer falls in the middle of flexibility and ease of use, and as an artist one needs the best of both worlds. Abstraction layer Building text-to-video pipeline we learn how we can create a pipeline to generate using the tools in the abstraction layer it is important to chalk down what will be steps to generate the video and what tools can be used in each layer. Before text-to-video AI text-to-video generation involves the following steps: 1. Generating scenes from the script using GPT prompts The first step I did was to few-shot-prompt GPT to give out the Hindi script in the form of dialogues between the characters involved in every scene. Input Output 2. Generating images from the scenes This is the crucial step and involves creating the images for the scene which was broken down from the script, it all boils down to how creatively we can express ourselves using the prompt guidelines of , the example below mentions the prompt given to Midjourney to generate an image for a scene. Midjourney A cartoon of scene where Indian old hindu saint is asking for a help with Lord Cloud; Lord cloud is personified and have happiness on his face, the environment around is full of trees with dark clouds and lightening all around 3. Adding animation to the image In case you need to add animation to the image you can use or to add character motion and scene animation. DiD RunwayML 4. Generating AI voice for the scene narration In this step, you can generate the AI voice for the narration using eleven labs, generally, these are Text to speech narration models using behind which may sound a bit robotic but solve the purpose of generating voice, one can make it more expressive and realistic from eleven labs paid version, for this story I needed hindi voice narration for which narration does a great job. Ai4Bharat Text to speech 5. Stitching the video clips and syncing the voice This is the last and simplest step to add the images in a video editor and sync the voice as per scene and narration timeline, tools like Canva, and Adobe Express do a great job here. Rough Cost of Video Production Above is the simplest breakdown of how you can quickly generate video from text using a few basic tools, for my example, I generated an almost ~ 3-minute video with 16 unique scenes, interesting would be to see the time and money I paid to generate this video: Midjourney cost ~ $0.05/image - 16*0.05 = $0.8 RunwayML ~ $0.02/image - 16*0.02 = 0.32 Canva ~ Free of cost since not used their premium artifacts Total Cost ~ $1 /video Comparing it with the new-age AI video editors like which charges almost $28/month for 180 minutes of creation, which would for a video length mentioned above. Fliki cost ~ $0.5 Need to bundle the offering Although the final cost of generating the video in the AI-based video editors seems less compared to the total cost incurred by using tools like Midjourney, RunwayML, etc, with added cost these tools provide flexibility and creativity to a video content creator and can help in generating some amazing videos which can be comparable to an amazing scene of Hollywood movie, it seems that if these AI tools can be bundled and integrated with the workflow of video agency or video production houses they can produce maximum value in video production, as Justine Moore, Partner in thread also reflects the same. @a16z this

AI Stack for Text-to-Video Generation

টেক্সট-টু-ভিডিও জেনারেশনের জন্য এআই স্ট্যাক

KI-Stack für die Text-zu-Video-Generierung

Pila de IA para la generación de texto a vídeo

AI Stack pour la génération de texte en vidéo

टेक्स्ट-टू-वीडियो जेनरेशन के लिए एआई स्टैक

テキストからビデオへの生成のための AI スタック

텍스트-비디오 생성을 위한 AI 스택

AI Stack para geração de texto para vídeo

Стек AI для преобразования текста в видео

Metinden Videoya Üretim için AI Yığını

Ngăn xếp AI để tạo văn bản thành video

用于生成文本到视频的 AI 堆栈

What is The True Price of Generative AI?

জেনারেটিভ এআই এর আসল দাম কত?

Was ist der wahre Preis der generativen KI?

¿Cuál es el verdadero precio de la IA generativa?

Quel est le vrai prix de l’IA générative ?

जेनरेटिव एआई की सही कीमत क्या है?

生成 AI の本当の価格はいくらですか?

제너레이티브 AI의 실제 가격은 얼마인가?

Qual é o verdadeiro preço da IA generativa?

Какова истинная цена генеративного ИИ?

Üretken Yapay Zekanın Gerçek Fiyatı Nedir?

Giá thực sự của AI sáng tạo là gì?

生成式人工智能的真正代价是什么？

Cover for the album by DALL·E 3 In this article, I'll share how I made a project over a weekend to release my album ( ). I'm not a trained musician or composer, but sometimes, tunes pop up in my mind. I jot them down, and then let the computer play them. https://evhaevla.netlify.app/ In 2021, I launched my album titled "Everyone is Happy, Everyone is Laughing." It's a simple album by an unfamiliar "composer" – that's me. I'm not just into music; I'm also a developer, mainly focusing on frontend work recently. I thought, why not combine these two loves? So, I set out to design a website to visually present my album. This article won't dive deep into every technical detail — that would be too lengthy and might not appeal to everyone. Instead, I'll highlight the core concepts and the hurdles I encountered. For those interested, all the code can be found on . GitHub Visual My album is composed for piano, making the decision straightforward. Imagine rectangles descending upon the piano keys. Anyone with a musical inclination has likely encountered numerous videos on YouTube depicting notes in this manner. A rectangle touches a key, illuminating it, indicating the precise moment to strike the note. I’m uncertain of the origin of this visual style, but a quick Google search predominantly yields screenshots of Synthesia. On YouTube, there are creators who manage to produce visually stunning effects. Viewing such videos is a treat, both from aesthetic and musical perspectives. Watch or . this this What will we need to implement? Keys Rectangles Audio Animation Let's tackle each point and put all of these into action. Keys Initially, I assumed that implementing the keys would pose the greatest challenge. However, a quick online search revealed a plethora of examples and guides on how to do just that. Aiming for a design with a touch of elegance, I opted for an created by . example Philip Zastrow All that was left for me to do was replicate the keys several times and establish a grid for the notes to glide across. I employed Vue.js as the frontend framework, and below is the component code. <template>
  <ul style="transform: translate3d(0, 0, 0)">
    <li :id="`key_${OFFSET - 1}`" style="display: none"></li>
    <template v-for="key in keys" :key="key.number">
      <li :class="`${key.color} ${key.name}`" :id="`key_${key.number}`"></li>
    </template>
  </ul>
</template>

<script setup lang="ts">
import { ref } from 'vue'
import type { Key } from '@/components/types'

const OFFSET = 24

const template = [
  {
    color: 'white',
    name: 'c' // Do
  },
  {
    color: 'black',
    name: 'cs' // Do-diez
  },
  {
    color: 'white',
    name: 'd' // Re
  },
  /* ... */
]

const keys = ref<Key[]>([])

for (let i = 0; i < 72; i++) {
  keys.value.push({
    ...template[i % 12],
    number: i + OFFSET
  })
}
</script> I'd like to mention that I appended an attribute to each key, which will be essential when initiating their animations. id Rectangles While this may appear to be the simplest segment, a few challenges are hidden in plain sight. How can the effect of descending notes be accomplished? Is there a need to maintain a structure that can be queried to retrieve the current notes? What’s the best approach to render the outcomes of such queries? Each question presents an obstacle to navigate to achieve the desired effect seamlessly. I won’t linger on each question but will rather cut straight to the chase. Given the myriad challenges associated with the dynamic approach, it’s wise to heed Occam’s Razor and opt for a static solution. Here’s how I addressed it: I rendered all 6215 notes simultaneously on a single expansive canvas. This canvas is housed within a container styled with the property. Achieving the falling notes effect is then simply a matter of animating the of this container. overflow: hidden scrollTop However, a lingering question remains: how do I obtain the coordinates for each note? Fortunately, I have a MIDI file where all these notes are archived, a convenience afforded by being the composer of the album. It boils down to rendering the notes utilizing the data extracted from the MIDI file. Given that the MIDI file is in binary format and I had no intention of parsing it myself, I enlisted the help of the library. midi-file The library is efficient in extracting raw data from MIDI files, but for my needs, that’s not sufficient. I aim to transform this data into a more accessible and application-friendly format to facilitate seamless rendering within the app. midi-file In a MIDI file, it’s not notes in the usual sense that I’m dealing with, but events. There’s a whole array of these events, but I’m primarily focused on two types: 'noteOn', which is triggered when a key is pressed, and 'noteOff', when the key is released. Both 'noteOn' and 'noteOff' events specify the particular note number that was pressed or released, respectively. Time, in the conventional sense, is absent in MIDI. Instead, we have "ticks." The number of ticks per beat is detailed in the MIDI file’s header. Indeed, there's more to consider. A tempo track is also present, containing ‘setTempo’ events that are integral to the process, considering the tempo can change during playback. My initial approach involved adjusting the animation speed of the property of the container to align with the tempo. scrollTop However, I soon realized this wouldn’t yield the expected outcome due to excessive error accumulation. A 'linear' time stretch proved more effective for animating the . scrollTop Even with the animation aspect sorted, the tempo still required incorporation. I addressed this by adjusting the lengths of the notes’ rectangles themselves. While not the optimal solution (manipulating speed would be ideal), this method ensured smoother operation. This solution isn’t perfect, mainly because I associate a tempo event with a note event based on whether they have the same or less time. This means that if another tempo event occurs while a note is still in the playing state, it will simply be disregarded. This could potentially introduce an error, particularly if a note is very long and there's a dramatic tempo change occurring during its playtime. It’s a trade-off. I’ve accepted this minor flaw as I’m focused on rapid development. There are instances where speed takes precedence, and it’s more pragmatic not to get entangled in every detail. So, we're equipped with the following information: The specific key number The precise moment the key is pressed The exact time the key is released The intensity or "speed" at which the key is pressed With these details at hand, I can pinpoint the exact coordinates on the canvas for every note. The key number determines the X-axis, while the commencement of the key press is the Y-axis. The length of the press dictates the rectangle's height. By using a standard div element and setting its position to 'absolute', I successfully achieved the desired effect. Audio I didn't intend to create a synthesizer for the piano, as that would have taken up a lot of time. Instead, I used an existing OGG file that had already been "rendered" and selected Native Instruments' for the sound library. The Grandeur Personally, I believe it's the finest piano VST instrument available. I embedded the resulting OGG file into a standard audio element. My main task then was to synchronize the audio with the animation of my note canvas. scrollTop Animation Before I could tackle synchronization, the animation had to be established first. The canvas animation is fairly straightforward — I animate the from an infinite value down to zero, using linear interpolation. The duration of this animation matches the length of the album. scrollTop When a note descends onto a key, that key illuminates. This means that for each note's descent, I need to "activate" the corresponding key, and once the note completes its course, deactivate it. With a total of 6215 notes, this equates to a whopping 12,430 note activation and deactivation animations. Additionally, I aimed to provide users with the capability to rewind the audio, enabling them to navigate anywhere within the album. To implement such a feature, a robust solution is essential. And when faced with the need for a dependable solution that "just works", my go-to is always the . GreenSock Animation Platform Look at the amount of code it takes to create all the animations for each of the keys. Using to animate components isn't the best practice for Single Page Applications. However, this method is a real time-saver. Recall the I mentioned for each key? This is where they come into play. id id const keysTl = gsap.timeline()
notes.value.forEach((note) => {
  const keySelector = `#note_${note.noteNumber}`

  keysTl
    .set(keySelector, KEY_ACTIVE_STATE, note.positionSeconds)
    .set(keySelector, KEY_INACTIVE_STATE, note.positionSeconds + note.durationSeconds - 0.02)
}) The synchronization code essentially establishes a connection through events between the audio and the GSAP global timeline. audioRef.value?.addEventListener('timeupdate', () => {
  const time = audioRef.value?.currentTime ?? 0
  globalTl.time(time)
})

audioRef.value?.addEventListener('play', () => {
  globalTl.play()
})

audioRef.value?.addEventListener('playing', () => {
  globalTl.play()
})

audioRef.value?.addEventListener('waiting', () => {
  globalTl.pause()
})

audioRef.value?.addEventListener('pause', () => {
  globalTl.pause()
}) Captions Just when I felt like wrapping up, an intriguing idea sprang to mind. What if I added a unique twist to the album? It wasn't originally on my to-do list, but I felt the project wouldn't truly shine without this feature. So, I chose to incorporate it as well. Every time I immerse myself in a track, I find myself reflecting on its deeper meanings. What message was the composer trying to convey? Consider, for instance, in "Nightbook" by Ludovico Einaudi. The piano resonates in the left ear, while the strings echo in the right. a segment It crafts an ambiance of a dialogue unfolding between the two. It feels as if the piano keys are probing: "Do you concur?" The strings affirmatively respond. "Is that the query?" The strings echo their affirmation. The sequence culminates with both instruments converging, symbolizing a realization of unity and harmony. Isn't that a mesmerizing experience? It's imperative to mention that this is purely my personal interpretation. Once, I had the opportunity to attend a Ludovico concert in Milan. After the performance, I approached him and inquired if he had indeed intended to embed the notion of a dialogue in that particular segment. His response was enlightening: "I never pondered it that way, but you certainly possess a vivid imagination." Drawing from that experience, I pondered: what if I integrated subtitles into the sheet music? As specific segments play, commentary could materialize on-screen, providing insights or interpretations into the composer's intent. This feature could offer listeners a deeper understanding or a fresh perspective on "what did the author truly mean?" It was fortunate that I chose GSAP as my animation tool. It allowed me to effortlessly integrate another timeline, specifically tasked with animating the commentary. This addition streamlined the process and made the implementation of my idea much smoother. I had an inclination to introduce the comments via HTML markup. To achieve this, I crafted a component that introduces the animation during the event. onMounted <template>
  <div :class="$style.comment" ref="commentRef">
    <slot></slot>
  </div>
</template>

<script setup lang="ts">
/* ... */

onMounted(() => {
  if (!commentRef.value) return

  props.timeline
    .fromTo(
      commentRef.value,
      {
        autoAlpha: 0
      },
      {
        autoAlpha: 1,
        duration: 0.5
      },
      props.time ? parseTime(props.time) : props.delay ? `+=${props.delay}` : '+=1'
    )
    .to(
      commentRef.value,
      {
        autoAlpha: 0,
        duration: 0.5
      },
      props.time ? parseTime(props.time) + props.duration : `+=${props.duration}`
    )
})
</script> The use of this component would be as follows. <template>
  <div>
    <Comment time="0:01" :duration="5" :timeline="commentsTl">
      <h1>A title for a track</h1>
    </Comment>
    <Comment :delay="1" :duration="13" :timeline="commentsTl">
      I would like to say...
    </Comment> Apotheosis With all the elements in place, the next step was to host the site. I opted for Netlify. Now, I invite you to experience the album and view the final presentation. https://evhaevla.netlify.app I genuinely hope there are other piano-loving developers out there, eager to showcase their albums in such a unique manner. If you're one of them, don't hesitate to fork the project.

Striking a Chord: The Art of Animating Music With Meaning

স্ট্রাইকিং এ কর্ড: দ্য আর্ট অফ অ্যানিমেটিং মিউজিক উইথ মিনিং

Einen Akkord anschlagen: Die Kunst, Musik mit Bedeutung zu beleben

Tocar una fibra sensible: el arte de animar la música con significado

Toucher une corde sensible : l'art d'animer la musique avec du sens

तार छेड़ना: अर्थ के साथ संगीत को सजीव करने की कला

コードを叩く: 意味のある音楽にアニメーションを与える芸術

코드 연주: 음악에 의미를 부여하는 기술

Tocar um acorde: a arte de animar música com significado

Взятие аккорда: искусство оживлять музыку смыслом

Akor Vurmak: Müziği Anlamlı Şekilde Canlandırma Sanatı

Đánh hợp âm: Nghệ thuật tạo nên âm nhạc sống động đầy ý nghĩa

敲响和弦：赋予音乐意义的艺术

While organizations around the globe have long gone on an AI investment spree, the number of artificial intelligence projects that make it from prototypes to production . still fluctuates around 53% Experts believe this often happens due to lacking tech skills, human resources, and tools to scale isolated proof of concepts (PoCs) across other use cases. And the presumably high cost of training separate AI models for different tasks, of course. AI Foundation models — i.e., large machine learning models trained on vast volumes of unlabelled data under the guidance of skilled AI consultants — may be the ultimate answer to the daunting AI scalability and cost problems. Your company could use such models as a starting point to enhance or automate various tasks, from converting paper-based documents into editable text files to . And build on your AI excellence from there, adapting foundation models for future tasks and use cases. uncovering customer sentiment in social media reviews What are foundation models, and how could they help your company excel at AI? Unless you’ve been living under a rock, you’ve heard about . This language model has absorbed tremendous volumes of conversational text using the supervised learning and, at the fine-tuning stage, the reinforcement learning from human feedback (RLHF) approaches. OpenAI’s ChatGPT The generative AI solution can analyze input data and profoundly understand the written language. The smart tool can answer questions, summarize and translate text, produce articles on a given topic, write code, and much more. All you need is to . against 175 billion parameters provide ChatGPT with the right prompts OpenAI’s groundbreaking product is just one example of foundation models that transform as we know it. AI application development Foundation models disrupt AI development as we know it. Instead of training multiple models for separate use cases, you can now leverage a pre-trained AI solution to enhance or fully automate tasks across multiple departments and job functions. With foundation AI models like ChatGPT, companies no longer have to train algorithms from scratch for every task they want to enhance or automate. Instead, you only need to select a foundation model that best fits your use case — and fine-tune its performance for a specific objective you’d like to achieve. Foundation models are perfect for industries where training data can be too hard or expensive to acquire. These industries include , life science, biotechnology, and manufacturing, to name a few. healthcare What types of foundation AI models are there? Several types of foundation AI models are commonly used in business applications: are trained on a dataset that contains a mixture of labeled and unlabeled data. The goal is to use the labeled data to improve the model’s performance on the unlabeled data. AI experts turn to semi-supervised learning when training data is difficult to obtain or would cost your company an arm and a leg. This, for instance, may happen in medical settings where various healthcare IT regulations are enacted. Some common examples of semi-supervised models include pre-trained text document and web content classification algorithms. Semi-supervised learning models are fully trained on unlabeled datasets. They discover patterns in training data or structurize it on their own. Such models, among other things, could segment information into clusters based on the parameters they’ve uncovered in a training dataset. ML engineers turn to auto-encoders, K-Means, hierarchical clustering, and other techniques to create unsupervised machine learning solutions and improve their accuracy. Unsupervised learning models interact with their environment without specific training. When achieving a desired outcome — i.e., making a prediction the developers have hoped for — the models get rewarded. On the contrary, reinforcement learning models are penalized when making wrongful assumptions. The approach allows AI algorithms to make more complex decisions than their supervised and semi-supervised counterparts. An example of reinforcement learning in action would be autonomous vehicles or game-playing artificial intelligence like AlphaGo. Reinforcement learning models produce new data similar to the data they’ve been trained on. This data may include text, images, audio clips, and videos. The ChatGPT solution mentioned in the previous section belongs to this category of foundation AI models. Other examples of generative AI include the , which creates images based on descriptions written in natural language, and the video platform, which uses text-based inputs to produce video content. Generative AI models DALL-E 2 tool Synthesia.io can solve tasks other than they’ve been trained on. For instance, computer vision engineers may leverage pre-trained image classification algorithms for object detection. Or harness existing NLP solutions for more knowledge-intensive tasks, such as customer sentiment analysis. Some popular pre-trained machine learning solutions include OpenCV, a computer vision library containing robust models for object classification and image detection, and Hugging Face’s Transformers library offerings, such as generative pre-trained transformer (GPT) — i.e., a rich language model whose third generation (GPT-3) powers the ChatGPT service. Transfer learning models , unlike their task-orientated equivalents, literally learn to learn (no pun intended). Instead of devouring data to solve a specific problem, such models develop general strategies for problem-solving. This way, meta-learning solutions can easily adapt to new challenges while using their resources, such as memory and computing power, more efficiently. ML experts tap into meta-learning when training data is scarce, or a company lacks definitive plans regarding AI implementation in business. TensorFlow, PyTorch, and other open-source machine learning libraries and frameworks offer tools that allow developers to explore meta-learning techniques. And cloud computing providers like Google help ML experts and newbies train custom machine learning models using AutoML. Meta-learning models Depending on the specific application and the type of data you have, one foundation model may be more appropriate than another. And your company is free to choose between an open-source solution, which needs a bit of tweaking, or a ready-to-use third-party product — provided it meets your business targets. Top 3 reasons to leverage foundation AI models for your next project Compared to standalone, task-oriented machine learning models, foundation models help create reliable AI solutions faster and cheaper, with less data involved and minimal fine-tuning. And that's not to mention that, being trained on more data than a single organization could ever obtain, foundation models display high accuracy from day one. Below you will find a rundown of foundation AI models’ advantages: . Creating and deploying an AI solution requires considerable time and resources. For every new application, you need a separate well-labeled data set. And if you don’t have it, you’ll need a team of data experts to find, cleanse, and label that information. According to Dakshi Agrawal, CTO of IBM AI, foundation models help cut down on data labeling requirements by 10-200 times depending on a given use case, which translates into significant cost savings. On the business side, you should also consider the rising cloud computing expenses. Google, for instance, spent as much as $35 million to teach DeepMind to play Go. And while your AI project may not be half as ambitious, you in cloud server costs alone to get your AI app up and running. Another reason to use foundation models, such as generative AI solutions, is the opportunity to quickly prototype and test different concepts without investing heavily in R&D. Foundation models will help you implement AI faster, cheaper, and with fewer resources involved could easily spend $300,000 . As their name implies, AI foundation models can serve as a basis for multiple AI applications. Think about driving a car. Once you’ve got a driver’s license, you don’t need to pass the exam every time you buy another vehicle. Similarly, you can use a smaller amount of labeled data to train a general-purpose foundation model that summarizes texts to process domain-specific content. And foundation models possess “emergence” capabilities, too, which means that a model, once trained, may learn to solve problems it was not supposed to address or glean unexpected insights from training data. You can reuse foundation AI models to create different applications . Training one large machine learning model footprint as running five cars over their lifetime. Such a heavy carbon footprint stands in sharp contrast with the fact that 66% and 49% of businesses are and developing new climate-friendly services and products, respectively. With foundation AI models, you can train intelligent algorithms faster and utilize computing resources wisely — not the least thanks to the models’ architecture that takes advantage of hardware parallelism, executing several tasks simultaneously. Foundation AI models help achieve your company’s sustainability goals can have the same environmental increasing the efficiency of energy use Deemed “the future of AI,” foundation models lower the threshold for tapping into artificial intelligence and could potentially end the failed AI proof of concept cycle by helping businesses scale models across other use cases and company wide. But with every opportunity comes a challenge. Things to consider when using foundation models The only glaring drawback of foundation AI models is a . lack of explainability Large foundation models can use so much training data and have so many deep layers that it’s sometimes hard to determine how algorithms arrive at their conclusions. The black-box nature of foundation models leaves a backdoor for cybercriminals, too. Hackers can launch data poisoning attacks and introduce , further exacerbating artificial intelligence’s ethical issues. AI bias Technology companies should join forces with governments to set up infrastructure for public AI projects to avoid contention surrounding foundation AI models’ usage. AI vendors should also disclose what datasets they use and how they train their models. As Percy Liang, Stanford HAI faculty and computer science professor, , “We’re very much in the early days, so the professional norms are underdeveloped. It’s therefore imperative that we, as a community, act now to ensure that this technology is developed and deployed in an ethically and socially responsible fashion.” opined during his recent interview with Venture Beat What does it take to start using foundation models in your organization As someone who’s spent the last ten years helping companies implement AI systems, the ITRex team is witnessing a shift in artificial intelligence. Systems that execute specific tasks in a single domain give way to broad AI that learns more generally and works across industries and use cases. Foundation models, trained on large, unlabeled datasets and fine-tuned for various applications, are driving this transformation. If your company is ready to leapfrog your competitors and get ROI from your AI systems faster, here’s a high-level strategy for implementing foundation models: . The first step involves collecting and pre-processing the data you will feed to a foundation AI model. The quality and diversity of this data are critical for ensuring that the fine-tuned model is accurate and robust. Collect and pre-process your data . Many pre-trained AI foundation models are available on the market. Some popular solutions include BERT, GPT, and ResNet, among others. It’s important to choose the right foundation model based on the task you want to solve and the type of data you have. Choose a foundation model . Once your foundation model and data are ready, you can adjust the model’s parameters to your specific task. One way to achieve this goal is transfer learning, where you use the pre-trained weights of the foundation model as a starting point and adjust them based on your training data. Tweak the model in line with your business objectives . After fine-tuning, it’s crucial to determine if the model works well and if further adjustment is necessary. To assess the foundation model’s performance, you can use standard metrics such as accuracy, precision, recall, and F1 score. Evaluate the model . Once you’re satisfied with the performance of your fine-tuned model, you can deploy it in a production environment. Several options for deploying AI models include cloud-based platforms, on-premise servers, or edge devices. Deploy your AI solution It’s important to remember that implementing AI foundation models requires technical expertise and access to specialized hardware and software tools. Therefore, it may be helpful to partner with a specialized AI vendor or consult with a team of AI experts to ensure the process is done effectively. to discuss your AI needs! We’ll assess your company’s AI readiness, audit your data and prepare it for algorithmic analysis, and choose the right foundation model for getting started with artificial intelligence! Drop us a line

Intro to Foundation AI Models: Types, Use Cases, and How to Get Started 

Introducción a los modelos básicos de IA: tipos, casos de uso y cómo comenzar

Introduction aux modèles Foundation AI : types, cas d'utilisation et comment démarrer

फाउंडेशन एआई मॉडल का परिचय: प्रकार, उपयोग के मामले और कैसे आरंभ करें

Foundation AI モデルの概要: タイプ、ユースケース、開始方法

Introdução aos modelos básicos de IA: tipos, casos de uso e como começar

Giới thiệu về các mô hình AI nền tảng: Các loại, trường hợp sử dụng và cách bắt đầu

基础 AI 模型简介：类型、用例以及如何开始

: User experience (UX) can make or break your website. of online shoppers say they won't revisit a site after a bad experience. And yet, many businesses make the mistake of thinking that a visually appealing website is all needed to engage visitors. But the truth is, a beautiful site won't make much of a difference if it's frustrating to use. This is where user experience design comes in, giving your website the clarity and intuitiveness needed to encourage conversions. Let's face it 88% In today's competitive digital landscape, optimizing your website's user experience is crucial to stay ahead of the game. But with so many elements to consider, where do you even start? What about the balance of design, functionality, and user behavior? Here we'll explore seven ways to optimize your website user experience and take it to the next level. What exactly is User Experience? I am just talking about user experience because "User Experience" (UX) has become increasingly popular over the last few years. But what exactly is UX, and why is it important? User Experience is how a user interacts with a product, service, or system. In the context of digital design, UX is concerned with creating a website or application that is easy to use, visually appealing, and achieves the user's goals efficiently and intuitively. A good user experience should consider the needs and expectations of the target audience, as well as the design and functionality of the product. 7 Ways to Optimize Your Website User Experience Let's get on the practical ways to create UX your target audience loves. optimize your website's user experience and 1) Streamline Navigation Navigation is a critical aspect of user experience design. use a website's navigation menu to orient themselves. 50% of visitors A clear and helps visitors quickly find what they want and move around your site. To improve your site navigation: easy-to-use navigation system Visitors should be able to understand what each menu item means without any confusion. Use straightforward labels for menu items: The menu items and categories should be organized in a logical and easy-to-understand order. It helps visitors to find the content they are looking for more easily. Create a logical and intuitive hierarchy: Including a search bar on your site can help visitors find what they are looking for quickly and easily. Provide a search bar: 2) Optimize Page Speed Just like me or anybody else, expect your web page . Visitors expect fast-loading pages, and and reduced engagement. But how can you improve your site's page speed? Well, there are multiple ways to do that; here are some standard practices: 47% of consumers to load in 2 seconds or less slow load times can lead to higher bounce rates Compress images: Large image files can significantly slow down your page load times. Ensure you to reduce their file size without compromising quality. compress your images Use caching: Caching stores frequently accessed data so that it can be served faster. It can and improve page load times. reduce server response time Minimize HTTP requests: HTTP requests are the number of requests the browser makes to load a page. If we , it may improve page load times. reduce the number of HTTP requests 3) Use Responsive Design Responsive design ensures your website looks great and functions well on any device, from desktop computers to mobile devices. It means your site automatically adjusts its layout based on the screen size it is viewing. To make your website responsive: Start by designing your site for mobile devices, and then scale up to desktop screens. . A flexible layout means your site's content adjusts automatically to the screen size. Go for flexible layouts Ensure your well on various devices, including smartphones, tablets, and desktop computers. site looks and functions 4) Provide Clear Calls to Action Calls to action (CTAs) are buttons, links, or forms. They encourage visitors to take action. Here are a few tips for creating effective CTAs: Your CTAs should stand out from the rest of your site's content, so visitors know exactly . what action to take Make CTA's language , so visitors understand what they will get by clicking on them. clear and simple Use to determine which CTAs are the most effective at driving conversions. A/B testing 5) Focus on Visual Design And Add Interactivity A visually appealing website can increase engagement, improve brand perception, and create a positive emotional response in visitors. To do that: can bring your site to life and create a more professional look. High-quality images The you choose can significantly impact your website's readability and overall look. typography , is the area between design elements. It can help create a clean, modern design, so use it effectively. White space, or negative space How do you deal with interactive visuals if you are not a professional and are on a low budget? You can with no effort. try Synthesia to make professional-looking videos 6) Incorporate Social Proof Incorporating social proof on your website can have a significant impact on enhancing the user experience. Social proof is when people look at others' actions and behaviors to determine their actions. a psychological phenomenon Seeing others engaging with your website and product can create in your brand, improving the user experience. a sense of trust and confidence 7) Create Engaging Content The content on your website plays a crucial role in user experience. Engaging content can capture visitors' attention and keep them on your site for extended periods. Being a content writer, I understand the importance of content for your website. Content is a factor that decides whether a reader will . So here is what I opt for creating engaging content: stay or skip ● Storytelling: Did you notice that ? Do you know why? It's human nature that people are naturally drawn to stories. Use storytelling techniques to create compelling content that resonates with your target audience. people like to read stories ● Break up content with subheadings and bullet points: What if this article is a single large paragraph of 1000 words? Are you going to take a look and skip immediately? So avoid this thing! Long blocks of text can be overwhelming for visitors. You can use subheadings and bullet points to break up content and make it easier to read. ● Include multimedia: Images, videos, and infographics can make your content more engaging and digestible. Include interesting visuals because this will seek user attention and helps to retain users. By implementing these strategies, you can create a user experience that keeps visitors engaged and leads to higher conversions. How can an optimized website help you get more $$? An optimized website is not just about aesthetics and functionality; it can also significantly impact . You can turn them into paying customers by attracting more visitors and engaging them with an exceptional user experience. It means an increase in sales and revenue. your bottom line This can be achieved by incorporating: clear call-to-actions, simplifying the checkout process, or leveraging personalization techniques The goal is to tailor the experience to the individual user. Final Thoughts By putting the user first, you can ensure that your website is the centerpiece of everything. Optimizing your website's user experience is more than just making it look good. What matters is to make it functional and easy to use. Start optimizing your website today and see the results for yourself!

7 Ways to Optimize User Experience on Your Website

Everybody has heard of ChatGPT, which has gained tremendous popularity in enhancing productivity. If you like ChatGPT, you check out these 10 other AI tools that can transform the way you work and live by automating tasks and improving productivity. Whether you’re in data science, business analysis, or simply looking to boost personal productivity, this article is a must-read for anyone looking to utilize the power of AI. HAVE TO 1. MidJourney: AI Art creates unique and imaginative artworks using Artificial Intelligence. With MidJourney AI Art, you can experience the union of technology and creativity, as AI technology produces captivating works of art that blur the lines between human and machine-generated art. Midjourney MidJourney offers a range of AI art services and products for businesses, collectors, and individuals looking to add a touch of innovation to their spaces. 2. Adobe Podcast AI VoiceAI-powered audio enhancer is a powerful tool that leverages Artificial Intelligence (AI) to enhance the quality of audio recordings for podcasts. With its advanced voice processing capabilities, it allows users to produce professional-sounding podcasts with ease. Whether you’re a seasoned podcaster or just starting out, Adobe Podcast AI Voice is the perfect tool to help you elevate the audio quality of your podcast and reach a wider audience. Adobe Podcast AI Voice 3. Synthesia.io: AI video generation platform is an AI-powered platform that offers automated video creation services. It leverages Artificial Intelligence algorithms to quickly and easily create personalized, professional-quality videos that engage and inform the audience. Whether you’re a marketer, salesperson, or just looking to create engaging content, is the perfect solution for creating high-quality videos without the need for extensive editing or design experience. The platform offers a user-friendly interface and a range of customization options, making it easy to create videos that align with your brand and messaging. Synthesia.io Synthesia.io 4. Descript: AI Video Editing is an AI-powered writing and audio/video editing tool. It enables users to create and edit written or audio/video content in a seamless and intuitive manner, without the need for complex software or editing skills. With Descript’s AI algorithms, users can transcribe, edit, and manipulate audio and video recordings, as well as generate captions and subtitles, all in real-time. Whether you’re a writer, podcaster, or video creator, Descript offers a wide range of tools and features that help you produce high-quality content more efficiently, saving you time and improving your workflow. Descript 5. Notion AI: Automatic Content Creation is an AI-powered personal productivity tool that helps users manage their tasks, projects, and notes in a single, unified platform. It uses Artificial Intelligence algorithms to automate repetitive tasks, provide intelligent suggestions, and help users stay organized. Notion AI offers a range of features including task management, project tracking, note-taking, and more, making it the perfect tool for individuals and teams looking to boost their productivity. The platform is highly customizable, making it easy to tailor to your specific needs, and offers integrations with other popular productivity tools, ensuring a seamless workflow. Whether you’re a freelancer, student, or work in a fast-paced corporate environment. Notion AI 6. Runway ML: AI-powered Machine Learning Tool for Creators is an AI-powered platform for creative and digital artists. It enables users to easily experiment with state-of-the-art Artificial Intelligence models, without the need for extensive programming or technical knowledge. Runway ML offers a user-friendly interface and a wide range of pre-trained models, making it easy to create innovative, visually stunning projects that leverage the power of AI. Whether you’re a graphic designer, photographer, or artist, Runway ML provides the tools and resources you need to push the boundaries of your creativity and explore new ways of working with AI. The platform is highly accessible and flexible, making it the perfect solution for artists and creatives looking to incorporate AI into their work. Runway ML 7. Profilepicture.ai: AI-Generated Profile Pictures is a cutting-edge AI tool that helps individuals and businesses optimize their profile pictures for social media and other online platforms. The tool uses Artificial Intelligence algorithms to analyze existing profile pictures and provide actionable insights and recommendations for improvement. You can easily enhance the quality of their profile picture, improve their online presence, and make a great first impression on potential clients, customers, or partners. The platform is user-friendly, fast, and accurate, making it the perfect solution for anyone looking to improve their online presence and make a great impression online. ProfilePicture.ai 8. RATH: AI-assisted Automated Data Analysis is one of the most ground-breaking AI assistants for Automated Data Exploration right now. Transforms traditional Business Intelligence by Automating the Data Analysis process. Empowered with an Augmented Analytics engine for all the data processing tasks, RATH can automate Data Visualization and Data Exploration process and generates valuable insights and recommendations. It is the Copilot for Data Analysis. RATH And, above all, RATH is Open Source Software. You can check out its source codes on . RATH GitHub 9. Fliki: Turn text into videos with AI voices is an AI-powered platform that helps organizations streamline their workflows and improve their productivity. It uses Artificial Intelligence algorithms to automate tasks, provide insights, and make intelligent recommendations, enabling teams to work more efficiently and effectively. Fliki offers a range of features including task management, project tracking, collaboration tools, and more, making it the perfect solution for organizations of all sizes. Fliki The platform is highly customizable, ensuring it can be tailored to meet the specific needs of your team, and offers integrations with popular productivity tools, ensuring a seamless workflow. Whether you’re a small startup or a large corporation, Fliki provides the tools and resources you need to streamline your workflows and boost your productivity. 10. Looka: AI-powered design platform is a powerful AI tool for businesses and entrepreneurs looking to create professional logo designs for their brands. The platform uses Artificial Intelligence algorithms to analyze existing logos and design trends, providing users with a wide range of logo design options that are tailored to their brand and industry. Looka Looka offers an intuitive interface and a user-friendly design process, making it easy for anyone to create a stunning logo design, even if they have no prior design experience. The platform provides a wide range of customization options, allowing users to fine-tune their logo design to meet their specific needs. Whether you’re launching a new business or rebranding an existing one, Looka provides the tools and resources you need to create a professional logo design that accurately represents your brand and appeals to your target audience. Conclusion AI can revolutionize our lives and work by automating tasks and boosting productivity. The top 10 AI tools showcased in this article demonstrate AI's versatility and power in art creation, data analysis, audio and video editing, and personal productivity. For such a fast-developing field as AI, we are expecting more and more exciting new products to come out in the near future.

Top 10 AI Tools to Check Out If You're Bored With ChatGPT

As the world becomes increasingly technology-driven, the integration of artificial intelligence (AI) in our daily lives is becoming more and more prominent. From virtual assistants to personalized recommendations, AI has changed how we interact with technology. In 2023, it’s safe to say that AI will continue to revolutionize how we live, work, and play. With the rapid growth of AI, it can be overwhelming to navigate the ever-growing landscape of AI tools and technologies. In this article, we’re going to highlight 7 of the most exciting and innovative AI tools you should try in 2023. 1. ChatGPT ChatGPT is an advanced language model developed by OpenAI. It allows you to have human-like conversations and more. Moreover, It is trained on a massive dataset of text from the internet and fine-tuned for various conversational tasks, such as answering questions and generating text. ChatGPT In Marketing As a growth marketer, you can use ChatGPT for a variety of tasks like Write attention-grabbing emails and subject lines in a few minutes. Composing emails: : You can use ChatGPT to write engaging social media posts. You can pick your tone and style to fit your brand. Social media content : ChatGPT can help you find keywords for your next article. You can further refine your search by requesting long-tail keywords. Find keywords Are you stuck and need help with your ad copy? ChatGPT can write ad copies for your Facebook, Twitter, Instagram, LinkedIn, and other platforms. Writing Ad copies: : Easily create article or blog ideas within minutes. You can pick your tone, length, and even mood. Content ideas Those are some of the things you can do with ChatGPT. However, the possibilities are endless. They currently have two versions. They have the free version and the paid version called . Try it today. ChatGPT Plus 2. TweetMonk TweetMonk is a user-friendly social media management tool that helps you create engaging content using AI. Furthermore, you can schedule threads and tweets. TweetMonk In Marketing Using TweetMonk, you can save thousands of hours with your Twitter marketing. Some of the things you can do with this AI include : Easily generate hashtags for your tweets. Hashtag Generator Using TweetMonk AI analytics, you can pick the best time to post your tweets. Post Timing: You can queue your tweets with one click or schedule them manually to save you time. Queue Content: Threads have become the go-to place for informative content. You can use TweetMonk to get ideas for your threads and increase engagement. Thread Ideas: Have you come across a tweet you liked? You can use TweetMonk to rewrite the tweet. Rewrite Tweets: Check your tweet performance to see how your tweets perform. Rich Analytics: They have a free account called Explore that allows you some basic features. The paid version starts from $8 and has a 7-day free trial. 3. AISEO Art AISEO art is an AI assistant that helps you turn your ideas into art. You can generate high-quality art in a matter of minutes. Moreover, this tool can help you create creative artwork at a low cost. Features Unlimited image generation. HD quality downloads. Watermark-free images. Some of the things you can do with this AI include Logo creation Posters Website Graphics NFT art Tshirt art and so much more AISEO art offers two pricing packages. The first one goes for $7/month billed yearly or $9/month billed monthly. For $7, you get 400 image generations per month, HD-quality downloads, and watermark-free images. The other package is $15/month billed yearly and $19/month if billed monthly. . Start generating HD images today 4. BlackBox Are you a developer? Then this AI tool is for you. According to their website, BlackBox is your AI-powered coding assistant to help you can code 10X faster. You can code in 20+ languages like Python, JavaScript, TypeScript, Go, Ruby, and others. Moreover, you can turn any question into code. They have a chrome extension that allows you to copy Code from Videos & Images or a shared screen on zoom/teams/meet. They have packages starting at 0.59/week. 5. BeautifulAI BeautifulAI is a tool that helps you create beautiful presentations in minutes. It can design and animate your slides and help you find ideas for your presentation. Moreover, they have 50+ smart templates that you can choose from. The AI adapts each slide’s layout as you add content, thus automating the majority of the work. Other features include Visualize your data. Search for inspiration from templates available. Easy edits Pre-built slides. BeautifulAi pricing starts at $12/month. Create stunning slides today. 6. ChatSonicAI ChatSonicAI is a conversational AI like ChatGPT where you can have human-like conversations. It is different from ChatGPT because it offers answers based on current events and trending topics in real time. Some of ChatSonic features Voice command. Chrome extension Generate art ChatSonic has a free trial that limits you to 6,250 words and 1 user. The other package starts from $19/month for 47,500 words and 1 user. Try ChatSonic 7. Synthesia Transform your text into beautiful HD videos. Synthesia is an AI that allows you to create engaging videos with AI avatars. You can use 120 languages, different templates, a screen recorder, a media library, and more. Features Professional actors 85+ AI avatars Web-based app accessible in your browser Beginner friendly Professional voiceovers Quality videos Free media library Screen Recorder Background music Mp4 downloads Video embedding Closed captions Easy updates Pricing starts at $30/month. Conclusion In conclusion, the world of AI is constantly evolving and expanding, and the tools and technologies we’ve highlighted in this article are just the tips of the iceberg. Whether you’re a business owner looking to streamline your operations or a tech enthusiast, these 7 tools are sure to impress. As you experiment with these AI tools, keep in mind that the true potential of AI lies in its ability to augment human abilities, rather than replace them. By harnessing the power of AI, we can work smarter, not harder, and achieve more in less time. Do you know any other tools? Drop them in the comment section. Was this article helpful? Support me through . Or feel free to reach out at khamisidigital@gmail.com. buy me a coffee

7 Game-Changing AI Tools in 2023

Artificial intelligence (AI) is rapidly transforming how businesses operate and interact with their customers. From automating routine tasks to transforming decision-making processes,  AI has the potential to significantly improve efficiency and effectiveness in the business world. In this article, I will discuss how AI is shaping the future of business and provide examples of some companies that are already implementing these technologies. AI for Automation Automation is likely to be the only major way AI will shape the future of business. This has many implications for automating business activities and affects a wide range of industries. The following are a few of the most common: 1. AI Chatbots Chatbots powered by AI are computer programs that simulate conversations with human users via text or audio. They can be used to automate customer service, answer questions, and even make recommendations, all of which were previously done by humans. Chatbots can also be used in marketing as they can be programmed to interact with customers and provide personalized services. IBM's Watson, Amazon's Lex, Google's Dialogflow, and Microsoft's Bot Framework are some of the most well-known and widely used chatbot service providers for millions of businesses. 2. Voice Recognition AI voice recognition software is also broadly used. Customer service is perhaps one of the most common applications of AI-based automation that is already in place. However, it is also widespread in voice-enabled search and navigation, controlling home automation systems, hands-free app control, and other applications. To give you an idea, Nuance's Dragon, SoundHound's Houndify, and wit.ai are some of the most extensively used voice recognition service providers. 3. Robotic Process Automation In addition to automating manual, repetitive, and time-consuming tasks, robotic process automation (RPA) products also sped up workflows considerably. Among the examples are data entry, document processing, and customer service interactions. Some of the largest companies applying it into practice are UI Path, WorkFusion, and Automation Anywhere. 4. Image Recognition Artificial Intelligence image recognition allows computers to recognize objects, scenes, and patterns in images by analyzing and interpreting them based on AI algorithms. This technology has several applications, including object detection and classification - which are typically done by humans - and facial recognition and image search. Amazon's Rekognition, Google's Vision API, and Clarifai are among the most popular and widely used image recognition services today. 5. Autonomous Mobility Autonomous mobility refers to the use of AI to enable vehicles to operate autonomously, without the need for a human driver. This technology has the potential to transform the way people and goods are transported, resulting in increased efficiency, safety, and accessibility. AI autonomous mobility is achieved through a combination of sensors, cameras, and other detectors that allow the vehicle to perceive its surroundings. The vehicle then uses AI algorithms to process this information and make decisions about how to navigate its environment, such as adjusting its speed, changing lanes, and avoiding obstacles. One of the primary advantages of AI autonomous mobility is enhanced safety. Autonomous vehicles have the potential to significantly reduce the number of road accidents by removing human error from the equation. Besides, by optimizing routes and lowering fuel consumption, the technology can help to reduce traffic congestion and improve overall driving efficiency. Another advantage of AI autonomous mobility is increased accessibility for people who cannot drive. Autonomous vehicles, in turn, can provide a new level of independence and mobility to the elderly, disabled, or those without a driver's license. This technology has been developed by Tesla, Waymo, Easy Mile, and Boston Dynamics, to name a few. AI for Data Analysis AI is also increasingly being used in data analysis to assist organizations in making better, faster, and more data-driven decisions. AI algorithms can process massive amounts of data and identify patterns and relationships that human analysts may not notice right away. This data can then be used to inform business strategies, improve operations, and stimulate innovation. Some of the more specialized applications for these tools encompass: 1. Predictive Analytics With the help of historical data, AI algorithms can predict events in the future. Using AI, companies can predict customer behavior, sales trends, and market conditions more accurately. A number of AI algorithms are embedded in Netflix and Spotify's algorithms to help the companies better understand their users' likes and dislikes. Based on this data, services can then provide relevant recommendations to their customers to keep them engaged by listening to music or watching series on those platforms. 2. Fraud Detection Large amounts of data can be analyzed by AI to detect fraudulent activities such as credit card fraud, insurance fraud, and cyberattacks. The technology can detect unusual patterns of behavior and flag them for further investigation, improving financial transaction security and protecting organizations from potential losses. Master Card has long used AI technologies to provide a fraud score for every transaction on its network as part of its anti-fraud efforts, making the information available to its issuers. Moreover, AI is used to detect fraud in combination with Mastercard's biometrics and behavioral analysis capabilities. 3. Predictive Maintenance Data from equipment and systems can be analyzed by AI algorithms to predict when they may fail. This enables businesses to take proactive measures to avoid downtime and lower maintenance costs. Every year, General Motors spends more than $1 billion on maintenance and $7 billion on capital improvements. To reduce maintenance costs and unplanned downtime, the company now uses AI-driven tools to support the widespread adoption of predictive maintenance across its over 140 global production sites[ ]. 1 4. Supply Chain Optimization AI algorithms can analyze supply chain data to identify inefficiencies and make recommendations for improvement. There are several applications for this today: to analyze data such as sales trends, consumer behavior, and market conditions to predict future demand, allowing companies to better plan their production and inventory levels. Demand forecasting – to optimize inventory management by lowering the risks of stockouts and overstocking. Inventory management - to optimize delivery routes and reduce transportation costs by analyzing data such as traffic conditions, delivery routes, and shipping costs. Delivery optimization - to provide real-time visibility into shipment status, allowing businesses to quickly identify and resolve any issues that may arise. Supply chain visibility - to analyze data such as consumer demand, shipping costs, and market conditions in order to optimize product distribution while lowering costs and increasing margins. Blue Yonder, Aera Technology, Kinaxis, and IBM Watson Supply Chain are a few of the most recognizable companies in the supply chain optimization area. Product distribution - AI for Customer Service AI is being utilized more and more to improve customer service and provide a better customer experience. Artificial intelligence technology can assist organizations in increasing the efficiency and effectiveness of their customer service operations while also providing customers with timely and personalized support. 1. Virtual Assistants AI virtual assistants can provide personalized support to customers, assisting them in finding information or completing tasks. Virtual assistants, for example, can be integrated into websites or mobile apps to assist customers with their shopping experience. As such, Walmart customers can ask Google Assistant to add specific items to their shopping carts or learn more about their shopping habits in the app[ ]. 2 2. Sentiment Analysis AI can be used to understand customer sentiments and opinions by analyzing customer feedback such as social media posts and survey responses. This data can assist organizations in identifying areas for improvement and providing better customer service. For example, the global FMCG company Mars utilizes tools to assess video content and analyze sentiment in online ratings and reviews[ ]. 3 3. Automated Voice Response AVR tools are used to provide automated customer service over the phone and interact with customers by providing audio responses to their questions or requests. These systems can be programmed to respond to simple questions and requests, provide callers with information such as current balances, allow customers to access specific account information, and, when necessary, redirect customers to an operator or customer service representative. Alvaria, Avaya, and GeneSys are three of the most recognized companies in this field. AI for Marketing AI is being used to increase the efficiency of marketing efforts in a variety of ways. AI technology can assist organizations in better understanding their customers, boosting campaign targeting, and measuring the results of their efforts. It is used in marketing in the following ways: 1. Customer Segmentation To create targeted marketing campaigns, AI algorithms can analyze customer data such as demographic information, purchase history, and social media behavior. This enables businesses to better understand their customers and increase the effectiveness of their marketing efforts. The Amazon Personalize automated segmentation system, which matches customers with the most compatible products tailored to their personal needs, is one of the most vivid examples. This is achieved through the use of special techniques that learn how users interact with items and then segment them based on their preferences. 2. Social Media Marketing To better understand customer sentiment and preferences, AI algorithms can analyze social media data such as posts, comments, and likes. This data can be used to boost social media marketing efforts, such as by creating more relevant and engaging content. Content optimization By analyzing data to uncover patterns and trends in user behavior, this technology automates the process of personalizing content to target individual users. AI-powered content optimization tools can assist marketers in creating more personalized, compelling content to engage and convert customers by understanding user preferences. This technology also assists marketers in optimizing content for various channels such as the web, mobile browsers, apps, and others. Some of the most well-known companies in this regard are Marketo Engage, BridgtEdge, and Evergage. AI for Legal work In the legal industry, Artificial Intelligence is being used to automate repetitive tasks, improve the accuracy of legal research, and provide more efficient and cost-effective services to clients. 1. Contract Review and Analysis AI algorithms can be used to review and analyze large volumes of contracts, assisting lawyers in identifying key provisions and clauses in a timely manner. When compared to manual review, this can save time and improve the accuracy of contract review. 2. Document discovery AI algorithms can be used to categorize and prioritize legal documents, such as those used in litigation discovery. This can help lawyers identify relevant documents more quickly and make more informed decisions. 3. Legal Research Legal research tools powered by AI, such as Westlaw and LexisNexis, can provide lawyers with timely and accurate information on legal precedents, regulations, and case law. This can assist them in conducting more effective legal research and providing more informed advice to clients. 4. Document Automation Artificial intelligence algorithms can be used to automate the creation of legal documents like contracts and agreements. When compared to manual drafting, this can improve the efficiency and accuracy of document creation. Kira, Ross, Cognitiv+, LawGeex, Luminance, and Legal Robot are some of the best-known tools/companies developing and selling these technologies today. AI for Creative Activities Artificial intelligence is being used in the creation of music and video to automate tasks, generate new content, and provide artists and content creators with new creative tools. 1. Music Composition AI algorithms can generate new music by either creating original compositions or recombining existing elements in novel ways. For example, AI can analyse existing music to determine the key elements of various genres and then use this knowledge to create new pieces. 2. Video Editing AI algorithms can be used to edit video footage automatically, such as selecting the best shots, removing background noise, and color correcting. In comparison to manual editing, this can save time and improve the accuracy of video editing. 3. Content Creation AI algorithms can be used to create new types of content, such as computer-generated imagery (CGI) or motion graphics. This can provide new tools for artists and content creators to create and experiment with new forms of content. 4. Music Production AI algorithms can be used to automate music production tasks like mixing and mastering. AI algorithms, for example, can be used to optimise the balance of different audio elements in a mix, thereby improving the overall sound quality of a recording. 5. Video Enhancement AI algorithms can be used to improve video content quality by removing noise, stabilizing shaky footage, and increasing resolution. This can provide tools for content creators to produce high-quality video content with less effort. Amper Music, AIVA, Flow Machines, Synthesia, Elai, and DaVinci Resolve are among the most well-known companies in this field. AI to Identify Customer Needs Last but not least, Artificial intelligence is being used to identify customer needs by analyzing massive amounts of customer data and behavior in order to gain insights into customer preferences, motivations, and needs. One of the most visible examples of this is currently taking place in the fashion industry, with some examples provided below: This online personal styling service recommends clothing items to customers based on their personal style, body type, and preferences using AI algorithms. Stitch Fix: To determine which styles and colors are most popular, the global fashion retailer uses AI to analyze data from sales, social media, and customer feedback. The data is then used to inform their design and production decisions. Zara: This online clothing rental service employs Artificial Intelligence to recommend styles to customers based on previous rentals, preferences, and events they are attending. Rent the Runway: To inform their design and production decisions, this global fashion brand employs AI to analyze consumer behavior and preferences, such as the most popular colors, styles, and materials. Tommy Hilfiger: An Indian online fashion and lifestyle retailer employs AI to recommend products to customers based on their browsing and purchasing history, as well as their preferences and style. Myntra: Conclusion To summarise everything mentioned above, Artificial Intelligence is rapidly transforming the business world and shaping the future of many industries. AI is assisting businesses in making more informed decisions, enhancing efficiency and productivity, and providing a more personalized and effective customer experience by processing and analyzing massive amounts of data. AI is used in a variety of applications, including data analysis, marketing, customer service, and legal work. In a rapidly changing business landscape, the use of AI in these areas is aiding businesses in gaining a competitive advantage and staying ahead of the curve.
Still, despite the numerous benefits of AI, there are concerns about its potential impact on the workforce, as well as on privacy and security. As AI technology evolves, it will be crucial for companies to consider these issues and take a responsible and ethical approach to AI use. Overall, AI is profoundly and far-reaching shaping the future of business, and businesses that can effectively harness its power will be well-positioned to succeed in the years ahead. Lead image developed by stable diffusion.

AI's Growing Influence and the Future of Business

174 AI Tools To Try

90% of developers don’t know about these websites There are so many websites to make the work of developers easier. Everyone doesn't know all. In this article, we will see . 25 Must-Visit Killer Websites For Developers 1. DevDocs DevDocs brings together numerous API documentation in a single, searchable interface. You will find docs related to various programming languages, technologies in one place. 2. ray.so This website allows you to take beautiful screenshots of codes. It also has a dark mode and some preloaded themes for different programming languages. You can also use its VS Code extension. 3. Poet.so This site is useful for developers who wish to post photos of their tweets on social media. Website transforms your tweet into a beautiful and editable design. 4. roadmap.sh This website provides roadmaps, guidelines, and other educational content to assist developers in choosing a path and directing their learning. It is very helpful for a beginner as well as a learner who needs guidance. 5. LambdaTest LambdaTest is a cloud-based cross-browser testing platform that lets you test web applications on over 2000 different browsers, operating systems, and devices. LambdaTest allows you to test cross-browser compatibility both manually and automatically. 6. daily.dev It is a platform where you can find so many good articles to read daily. It shows the best articles from various platforms directly in your feed. Stay updated with quality content from various platforms directly into your browser by using its extension. 7. Showwcase Showwcase is a new social media website specially for people who code connect, build community, and find new opportunities. It is a LinkedIn like platform but only focused on developers. You can join and there. follow me 8. CSS-TRICKS You can master your web development skills by learning every basics about CSS from this website. If you didn't already know, CSS is what makes pages on the web look beautiful. 9. Medusa An open-source Shopify alternative. Medusa is an open-source headless commerce engine that is fast and customizable. It's Free. 10. Smartmockups Why give your time learning how to use Photoshop. Smartmockups allow you to generate gorgeous high-resolution mockups straight in your browser and across numerous devices using a single interface. The quickest web-based mockup tool available, No prior experience or abilities are required, and the library of professional mockups is always growing. 11. Code Beautify Make your source code more beautiful and easy to read using code to beautify its AI technology will make your source code more attractive and easy to read. 12. OverAPI For all developers, OverAPI is one of the most beautiful and useful websites. This website has cheatsheets for the majority of programming languages. Take a look at it right now. 13. Responsively This is an open-source website that speeds up the development of web apps by 5 times. All target screens are displayed side by side in a single window. Reduces the time it takes to develop! 14. Color Hunt Color Hunt is a website that allows you to select gorgeous colour palettes. Web designers will find it very beneficial to choose beautiful and appealing colours, as well as to accept colour recommendations from other artists and art projects. 15. Carbon This website will help you to create and share beautiful images of your source codes. It also allows you to make changes to the photographs. You should give it a go right now. 16. Meta Tags It is a website tag generator that also allows you to update and experiment with your content before seeing how it will appear on Google, Facebook, LinkedIn, and other social media sites. 17. Profile Pic Maker This website creates a professional and beautiful profile for each photo in your Web developer portfolio. As a developer, you will be exposed to social media. You only need to upload any of your images, and it will edit and enhance them. Give it a go right now. 18. Small Dev Tools This website offers a variety of free developer tools, including encoders/decoders, HTML/CSS/Javascript formaters, minifies, fake or test data generators, and more. It also allows you to share codes. 19. resume.io This website allows you to create a quality resume. It will help you to create a professional resume by providing you with well-tested templates. Give it a Try. 20. CODEPEN CodePen is a web-based development platform that allows users to modify front-end languages such as HTML, CSS, and JavaScript without having to install any software. The nicest aspect is that the findings are visible in real-time, making troubleshooting easier. Developers and designers can also exchange code samples (known as pens) with the rest of the world. 21. Storytale Storytale is a website that had collections of illustrations that Make your project eye-catching with awesome characters. It can be used personally and commercially. This is a helpful website for web designers. Get High-class assets for web and mobile projects. 22. readme.so This website allows you to create project README very easily. Its simple editor helps you to quickly add and customize all the sections you need for your project's README. You can use this to create documentation of your open-source projects quickly. 23. Peppertype Peppertype is a new tool that helps in automating the content production and ideation process. It analyses your business operations, understands your brand and target audience, and then creates fresh content for you using superior machine learning and artificial intelligence. If you are a developer creating content regularly, then this is for you. 24. synthesia Synthesia helps you to create AI-based videos in over 50+ languages. You don't need a camera, microphone, or real face to create video content. You just need to upload your script and you will get an AI video. 25. Programmer Humor Even developers also need entertainment. You can visit this website for various memes and jokes related to programming and developers. Have Fun. : I used screenshots taken by in this Article. Bonus Website Screely If You ❤️ My Content! Connect Me on Twitter This article was also published here

25 Useful Websites Developers Don't Use Enough

Low budget or no budget? Go with these fonts. They are free and come with a commercial license. Do you need a third? Previously, I talked (read: rant) a lot about buying . At the end of the day, you end up paying only a few cents for each font. But even if that looks out of your reach, I have these 26 beautiful fonts with commercial licenses and complete font families. font packs with complete licenses *In no particular order: 26. One Day One Day is a slim uppercase typeface designed by Nawras Moneer. It is fresh, modern, simple — it’s not too serious and has just the right amount of class. 25. Casual This font deceives you with its simplicity. Once you start using it, you will begin to see all the details each letter comes with. and are the designers behind this casual font. Pelin Maravent Gülsah Cansever 24. Dpopper This cute-cute font with a comic 3D look is designed by Iordanis Passas. Sky is the limit with this font, like literally. 23. Facunda Here is another 3D font with a dark details. It comes with five different styles. Facunda is a tall, dark edged, sans serif font designed by Bu. 22. Alcubierre If I had to use one font all my life, I would go with Alcubierre by Matt Ellis. Its thin, straight, geometric but not harsh, sleek but not too sharp, its minimalist look can go a long way depending on how you use it. 21. Amada This font, designed by Edu Oliveira, has a story behind it. The designer was inspired by the lettering on a wall in the streets of Limoeiro, his hometown. This no-nonsense font can be used a number of ways! 20. Athene The classic-classic serif font which is amazingly , Athene, comes with accents, numbers, and absolutely adorable lowercase style. The clean, geometric look goes perfect with minimalist, even corporate, designs. easy to pair 19. Atifa It is an all-caps serif font with handwritten feel. The detailing is absolutely spot on. You can see the ink getting thicker and thinner with the pressure of the hand in different letters. Love the inconsistency! 18. Besom Besom is created by & . It’s a handwritten brush font with a quite nostalgic vibe. It just with all the polaroid-pictures, 90’s vibe theme you have in mind. KRISIJANIS MEZULIS Gatis Vilaks goes 17. Blow Petar Acanski designed this bold and bubbly brush typeface inspired by the hip hop culture and graffiti art. To maintain legibility and clarity, Acanski added a few variations to the font. You can download it from the link above. 16. Cute Punk Cute Punk comes with three different weights. Accents are also available in the font. Depending on how you use it, you can take this and give it a retro spin or a K-Pop, bubblegum vibe. 15. Lena See those easy curves? That is the mark of a quality font. It’s geometric yet it is soft, it is simple yet makes a statement. It is perfect! 14. Mightype Taking you to Tuscany’s vineyard (not even sure if they have those) - the way this font curls up at the edges, it just makes you want to hit the download button. 13. Milkshake Laura Worthington came up with Milkshake. It comes with a lot of bonuses, and 8 alternatives to each character. It is like stealing! 12. Montepetrum Available in three weights, Montepetrum is a the font sans serif, thin-lin dreams are made of. If , or are just not doing the job — trying Montepetrum would not hurt. Lato Existence Light Alcubierre 11. Rainwood IanMikraz Studio are behind Rainwood, a handmade brush font which comes with 232 glyph, 52 alternate characters, 2 stylistic alternates, standard ligatures and more! 10. Verona Lotte This hand-lettered, brush font is the reason why hand-lettered fonts look so beautiful. Zoom and see the inconsistency in letters, it does not look like a fake, made-up-to-look-like-handmade font. The roughness in the letters makes it ten times more elegant. 9. Salvador With varying thickness, subtle handmade detailing, and the slight curve in each letter makes Salvador a constant favorite. 8. Shellahera Made with a brush pen, ShellaHera is a beatiful script font with a varying baseline. Lostvoltype also has a full-version of the font available. 7. Wildera This adorable, little script font comes in uppercase, lowercase and ligatures. The flourishes and the defined kerning makes it a must have font. 6. Joli Joli is not everybody’s come of tea. This sky-scraper like structure of the font makes it a unique choice. It is extremely current and stylish. One can do wonders with it. 5. Kano The unique think about Kano is the geometric edges. Rather than looking serious and straightforward, Kano has a quirky feel to it. Full marks for innovation! 4. Kirvy This sans serif font family comes with four weights. It is straight and geometric with round, softer edges. Works perfect no matter what the scale is. 3. Selima Another brush script typeface. It comes with rough edges which makes the lettering look super authentic. It’s a bit on the bold side, still it manages to maintain the elegant and delicate look. 2. Reckless Reckless is a brush sans serif typeface. This straight, slightly italic font is heavily textured. thank Nadi Spasibenko is the designer behind this font. 1. Synthesia Synthesia (or FloGreen as it shows up in the library) is a narrow looking sans serif font with rounded edges. It has a touch of vintage vibe and can be used a number of ways. If you have downloaded these fonts and are looking for inspiration to use them — you can here. further read

Too Good To Be True: These Free Fonts come with a Commercial License

I’ve spent the past few months trying to teach myself the piano, pretty much from scratch. It’s been tough, and sometimes disheartening, but also hugely rewarding. You might be wondering by now what an article about learning to play piano is doing on a tech blog. The answer is simple: I might not still be trying if it weren’t for technology. Though I occasionally feel like I’m in way over my head starting out as a 27-year old, and traditional methods are still the core part of this musical journey, I’m lucky to live in a time where a wealth of resources are available to me at the click of a button. This is intended to be the first part of a long-term series on learning the piano. It’s primarily a personal account, but I hope it might just help someone else starting out too. Starting out This personal goal has been a long time coming. Although I have no formal background in music, I have a fair bit of background. Nearly half of the music I listen to is classical, and I go to live performances several times a year. I took a couple of music history courses in college, even though I couldn’t read music better than a 3rd grader, let alone play it. Everything I knew about the technical side of music until now came from occasional my Wikipedia-fueled curiousity. informal To be totally honest, this isn’t my first go at learning the piano; I spent many an hour banging away at cheap keyboards when I was about 11. My parents bought me a second-hand beginner Casio, the kind with built-in games that teach you how to play. technically Thanks to those piano games, I could play a mean Twinkle, Twinkle Little Star, and I learned about a rudimentary quarter of Fur Elise. I was very proud the latter fact because I share a birthday with Beethoven. It wasn’t much, but I credit those cheesy MIDI tunes for sparking my initial interest in classical music. But then I stopped playing. My parents couldn’t afford a teacher, and I moved around too much for lessons to be viable anyway. In an era before smartphones, tutorials, ebooks, and modern access to information, trying to teach myself piano felt like a Herculean task I wasn’t quite up to. YouTube 16 years or so later, I decided I wanted to be more than a simple listener; I wanted actually the music I like so much. Perhaps even make my own. understand So, I googled. The Piano First, I needed something to actually play on. I looked to internet communities for advice on picking a piano and starting off as a beginner; became a particularly valuable resource. /r/piano on Reddit After an inordinate amount of research, I settled on Roland’s GO:Piano, a 61-Key instrument aimed at beginners that sells for around $329. It’s light and compact, has Bluetooth, can work with AA batteries, and the keys are the same width as a real piano. I liked Roland’s piano sounds better than anything else for its price, and the extra tones were neat too. I’m particularly fond of the Jazz Scat: My only real annoyance is that the headphone port is on the back, which seems like an odd choice for a keyboard designed to help beginners practice. It’s worth mentioning my choice somewhat went against the advice of many a piano forum member, who are quick to point you towards an 88-key unit with fully ‘weighted’ keys. Most digital keyboards — including the Go:Piano — use springs to push keys up. Weighted keys try to replicalicate the complicated mechanism behind a grand piano’s keyboard action, for more responsive playing that translates better to an acoustic piano. Reddit has a great list of recommendations, but pretty much no new digital pianos below $400 have that feature. Still, I chose the Roland for its portability and versatility, figuring I’d upgrade when necessary (more on this later). A small apartment means I needed something compact and easy to manuever. I usually stored the Go:Piano under my desk, and often played the keyboard on my lap if my desk had too many contraptions on it. My first priority was getting something practical so I’d actually play consistently, and on that front. I think I made the right choice. Learning After some research on a good book, I settled on Faber’s Adult Piano Adventures. That’s partly because because the Go:Piano came with a discount for it, but mostly because Faber was an established name that also happened to have a nifty app (an version is reportedly in the works) and lesson videos. It was the right combination of traditional and new-fangled I was looking for. iOS Android To be clear, the bulk of my time was still spent with the book, which combines theory, technique, and lessons, but the app was a useful resource for the moments I got stuck — and all-around making the process more fun. I connected the app to an Mini via Bluetooth, although a wired connection works too. There are a few notable features: iPad You can control the tempo/metronome, loop difficult sections, resize the sheet music. The optional keyboard view lets you see exactly which notes to hit, which helped me learn to read music more quickly — I use it less now You can isolate each hand’s part or only listen to the accompaniment. You can also change the volume for each part. You can make the music wait for you to play the right note. A dynamic tempo mode changes the speed of the music to match your own playing pace. This was my preferred was of learning to play with accompanyments, until I get the music up to the default tempo There are hundreds of practice songs at different levels When I was have trouble with a particular piece or passage in the book, I was able to turn to the app to help me figure out a tune and reinforce it section by section. To supplement my piano playing, I bought an introductory book on Music Theory. There’s no particular reason I chose this book other than the fact it was one of very few I could find with an audiobook version on Audible. I do most of my reading through audiobooks nowadays, and it’s a huge time-saver to be to read something educational while doing menial activities. Those things might seem like a small difference over sticking to a book, audio files, and a metronome, but it’s the of Bluetooth and the app that helped make sure I was practicing nearly every day, and being able to switch things up kept it interesting. It’s because I can listen to an audiobook on my way to the gym that I’m learning even when I can’t pratice. I’ve been learning with an almost obsessive focus, and I’d almost certainly be a few units behind if it weren’t for the added convenience technology affords me. convenience Drawbacks and resources But technology can be overwhelming too. In my first month or so, I tried to use _every_resource out there. I was watching tutorials on YouTube, reading a variety of music education sites, researching advice on forums, using learning apps and playing a few different piano “games”. There’s an app called Synthesia, for instance, which is popular for helping you play piano in a Guitar Hero style. I realize a lot of people use it with results for learning individual songs, but I simply found it counterproductive for my more theory-oriented goals. Which is why, as in all things, moderation is important. I eventually realized I was and am making consistent progress simply working through my two books, the Faber app, and a sightreading app; there’s no need to add superflous distractions if I’m already making progress. If you’re interested in starting out yourself, I’d recommend you stick to a couple of established resources to not be overwhelmed by the wealth of content out there. That said, here are a few useful resources I found during my own practice beyond the ones I mention above: Reddit’s piano FAQ and buying guide MusicTheory.net: A Great free resource on music theory Ear Training and Rhythm Practice: (Android) or (iOS) Perfect Ear Tenuto : Hundreds of useful videos covering all sorts of topics related to pianos. Living Pianos Videos Going to live performances. Seriously, there’s nothing quite like hearing an accomplished pianist play something live. What’s next Working in the tech media sometimes feels like technology is always out to make us depressed, ill, or dead. It’s good to remember that put to the right use, it can be fulfilling, too. There are a few things I intend to do as I move forward. I’m moving on to a full-size, weighted keyboard to get my muscle memory working with something closer to a real piano. I’ll be getting an honest-to-goodness piano teacher within the next couple of months to make sure I’m developing good habits. One of the great laments of being a post-college adult is not feeling like I’m simply something new. It’s great to have that feeling back. See you in a few months. learning This story was written by , and was originally published on . Napier Lopez The Next Web To read the weekly trending TNW story on Hacker Noon, visit . Hackernoon.com/tnw

How to Learn Piano With Technology

From Pen to Screen: a Step-by-step Guide on How to Use AI to Create Educational Videos

পেন থেকে স্ক্রীন পর্যন্ত: শিক্ষামূলক ভিডিও তৈরি করতে কীভাবে aI ব্যবহার করবেন তার একটি ধাপে ধাপে নির্দেশিকা

Vom Stift zum Bildschirm: Eine Schritt-für-Schritt-Anleitung zur Verwendung von aI zum Erstellen von Lehrvideos

Del lápiz a la pantalla: una guía paso a paso sobre cómo utilizar la IA para crear vídeos educativos

Du stylet à l'écran : un guide étape par étape sur la façon d'utiliser l'IA pour créer des vidéos éducatives

पेन से स्क्रीन तक: शैक्षिक वीडियो बनाने के लिए एआई का उपयोग कैसे करें, इस पर चरण-दर-चरण मार्गदर्शिका

ペンから画面へ: aI を使用して教育ビデオを作成する方法に関するステップバイステップ ガイド

펜에서 화면으로: AI를 사용하여 교육용 비디오를 만드는 방법에 대한 단계별 가이드

Da caneta à tela: um guia passo a passo sobre como usar IA para criar vídeos educacionais

От пера к экрану: пошаговое руководство по использованию искусственного интеллекта для создания образовательных видеороликов

Kalemden Ekrana: Eğitim Videoları Oluşturmak İçin Yapay Zekanın Nasıl Kullanılacağına İlişkin Adım Adım Kılavuz

Từ bút đến màn hình: Hướng dẫn từng bước về cách sử dụng AI để tạo video giáo dục

从笔到屏幕：如何使用人工智能创建教育视频的分步指南

Let's take a look at how one may setup an ad-hoc, local (offline) text-to-speech synthesizer  using , piper-whistle and named pipes. piper To setup piper on a GNU/Linux based system, I'll describe a general architecture using named pipes, which is straight forward enough to allow for system wide text-to-speech, with a little bit of manual setup, the help of piper-whistle and some minor trade-offs (it's simple, yet it won't support parallel speech processing). To start, let's fetch the latest piper stand-alone built from its ( at the time of writing this). After downloading the compressed archive, we'll create a directory structure for our piper setup. The root directory shall be at with the following sub-directories: repository hosted on github 2023.11.14-2 /opt/wind (will house the piper built) /opt/wind/piper (will contain the named pipes) /opt/wind/channels After decompressing, the piper executable should be available at , as well the accompanying libraries and . /opt/wind/piper/piper espeak-ng-data For managing voice models used by piper, I'd recommend using piper-whistle, a command-line utility written in python, which makes it more convenient to download and manage voices. You can get the latest wheel file from its or release page, or use the most recent release available through pip via . After installing piper-whistle, let's fetch a voice to generate some speech. First we’ll update the database by calling . For English speech, I quite like the female voice called alba. Using whistle, we can get a list of all available English (GB) voices using . The voice is at index 2. So to install it, simply call . gitlab github pip install -U piper-whistle piper_whistle -vR piper_whistle list -l en_GB piper_whistle install en_GB 2 Next, let's create the necessary named pipes. The resulting structure will look like this: (accepts JSON payload) /opt/wind/channels/speak (read by piper) /opt/wind/channels/input (written by piper) /opt/wind/channels/ouput To create a named pipe, you may use the following command: mkfifo -m 755 /opt/wind/channels/input Finally, we create three processes in separate shells: tty0: tail -F /opt/wind/channels/speak | tee /opt/wind/channels/input tty1: /opt/wind/piper/piper -m $(piper_whistle path alba@medium) --debug --json-input --output_raw < /opt/wind/channels/input > /opt/wind/channels/output tty2: aplay --buffer-size=777 -r 22050 -f S16_LE -t raw < /opt/wind/channels/output The process on tty0 makes sure, the pipe is kept open, even after the processing by piper or has been finished. This way, we can queue TTS requests and subsequently play or save them. aplay Since piper-whistle offers additional features if you use the structure above, we can now generate speech via . On systems with X11 you may generate a spoken version of the text in your clipboard via . piper_whistle speak "This is quite neat" piper_whistle speak "$(xsel --clipboard --output)"

How To Set up Piper Speech Synthesizer on a Local Machine

স্থানীয় মেশিনে পাইপার স্পিচ সিন্থেসাইজার কীভাবে সেট আপ করবেন

So richten Sie Piper Speech Synthesizer auf einem lokalen Computer ein

Cómo configurar el sintetizador de voz Piper en una máquina local

Comment configurer le synthétiseur vocal Piper sur une machine locale

स्थानीय मशीन पर पाइपर स्पीच सिंथेसाइज़र कैसे स्थापित करें

ローカル マシンで Piper Speech Synthesizer をセットアップする方法

로컬 시스템에서 Piper Speech Synthesizer를 설정하는 방법

Como configurar o sintetizador de fala Piper em uma máquina local

Как настроить синтезатор речи Piper на локальном компьютере

Yerel Makinede Piper Konuşma Sentezleyicisi Nasıl Kurulur

Cách thiết lập Bộ tổng hợp giọng nói Piper trên máy cục bộ

如何在本地计算机上设置 Piper 语音合成器

Realistic Text-to-Speech Voice Synthesis: Comparing Tortoise and Bark

In the realm of text-to-audio AI models, the race to create more realistic, immersive experiences has been heating up. A promising runner in this race is AudioLDM, which uses some novel techniques to generate speech, sound effects, or music from text or audio prompts. But how exactly does AudioLDM achieve this? And more importantly, what does the way in which the tech works tell us about the kinds of products we can build with it? In this article, we'll study AudioLDM to see what makes it special and understand how we can take advantage of those unusual and special capabilities to build interesting new startup products. Let's begin. Subscribe or follow me on Twitter for more content like this! AudioLDM at a glance LDM stands for Latent Diffusion Model. AudioLDM is a novel AI system that uses latent diffusion to generate high-quality speech, sound effects, and music from text prompts. It can either create sounds from just text or use text prompts to guide the manipulation of a supplied audio file. Check out from the 's project website, showing off some of the unique generations the model can create. I liked the upbeat pop music the best, and I also thought the children singing, while creepy, was pretty interesting. this video demo creator Key features of AudioLDM: Uses a Latent Diffusion Model (LDM) to synthesize sound Trained in an unsupervised manner on large unlabeled audio datasets Operates in a continuous latent space rather than discrete tokens Employs Cross-Modal Latent Alignment Pretraining (CLAP) to map text and audio Can generate speech, music, and sound effects from text prompts a combination of a text and an audio prompt or Allows control over attributes like speaker identity, accent, etc. Synthesizes realistic sound effects and music Creates sounds not limited to human speech (e.g. nature sounds) Generates audio samples up to 10 seconds long In a nutshell, AudioLDM pushes boundaries in controllable audio synthesis and lets us build some innovative text-to-audio products and services that were previously difficult to build. There are opportunities in gaming, audiobooks, accessibility, and beyond. Let's see a few. AudioLDM Use Cases There are lots of world-changing AI products just waiting to be built, all powered by AudioLDM. The capability to create high-quality, customizable synthetic sound isn't just an interesting theoretical outcome to read about on Arxiv—it also enables a host of new impactful applications for text-to-audio technology. Here are a few: : With AudioLDM, you can break the shackles of generic gaming dialogues. Imagine games where every character has a unique voice and speech pattern. Your games will no longer be about just play—they become living, breathing digital realms filled with distinct personalities that heighten immersion for gamers. You can also use the model to create sound effects from just a text prompt. Unleash Immersive Gaming Experiences : It's time to take the listener on a journey. Use AudioLDM to create audiobooks where each character speaks in a unique voice. Transform the listening experience from a monologue to a dynamic conversation, and create theatre-like immersion for your audience. Revolutionize the Audiobook Landscape : Learning is personal, and so should be the learning platforms. Use AudioLDM to offer personalized voices that cater to individual learner needs in your language learning apps or online media. Make the learning process more efficient, enjoyable, and inclusive. Reimagine Learning Platforms : With AudioLDM, you can make technology more accessible for individuals with disabilities. Whether it's screen readers or voice assistants, you can customize and control the synthesized speech to make your user experience more personal and effective. Drive Accessibility Forward As you can see here, there are a lot of potential startups waiting to be built. How are you going to use AudioLDM to build a text-to-audio product that will reshape your industry? Let's peel back the layers to understand what makes AudioLDM tick, and how you can harness its power to fuel your startup's growth. The Technology Behind AudioLDM If you're considering AudioLDM as a tool to create your next product, you need to understand the distinct technological principles that make this AI model unique. AudioLDM's text-to-audio capabilities are built on three pillars: its usage of 'unpaired learning', its operation within a 'continuous latent space', and a special technique known as 'Cross-Modal Latent Alignment Pretraining' (CLAP). Each of these elements contributes to its ability to synthesize controllable, high-quality sound. Let's look at each element in more detail to understand why they matter. Unpaired Learning Most AI models expect tidy datasets of aligned input-output pairs during training - for example, large tables of audio clips perfectly matched to their text transcriptions. AudioLDM rips up this playbook, learning entirely from audio data without any accompanying text. unpaired It relies on a clever self-supervised pretraining scheme. The model tries to reconstruct audio waveforms from compressed latent representations of those sounds. By minimizing the reconstruction error, it's forced to encode all the most salient features of natural speech into the latent space. Once AudioLDM has learned to represent audio data in this compact form, it can then condition its internal generative model on text prompts. This unpaired approach means we bypass cumbersome labeled datasets, instead unleashing the model on oceans of unlabeled audio data. Latent Space vs. Tokens Many speech models generate audio as a series of discrete, categorical outputs - like putting together Lego blocks to form sounds. But AudioLDM breaks free of this restrictive paradigm. It operates in a smooth, continuous latent space obtained by condensing audio spectrograms using a variational autoencoder (VAE). A spectrogram is a visual representation of the spectrum of frequencies in a sound over time. It displays the intensity levels of different frequencies as they vary through an audio clip. Spectrograms are created by applying a Fourier transform to break down sound waves into component frequencies. A is a type of neural network used for dimensionality reduction and generative modeling. It consists of an encoder and a decoder. The encoder compresses input data into a lower-dimensional latent space. The decoder tries to reconstruct the original input from points in the latent space. VAEs are used in many different models, and are often used in image generators like . variational autoencoder (VAE) Anything v3 VAE In a VAE, the encoder outputs a mean and variance for each latent dimension. These define a Gaussian distribution in the latent space. Sampling from these distributions and passing samples through the decoder allows generating new data similar to inputs. So for AudioLDM, the VAE condenses audio spectrograms into a smooth latent space. Interpolating between latent vectors mixes different voices and sounds. The decoder converts latent points back into realistic audio by leveraging patterns learned from training data. This gives AudioLDM its expressive generative capabilities. This continuity also encodes natural variation and uncertainty inherent to speech. Sampling latent points results in diverse outputs, even when the text prompt stays fixed. And lower-dimensional latent modeling greatly reduces computational overhead. Bridging Modalities with Cross-Modal Conditioning Here's the trick that lets AudioLDM synthesize speech without explicitly training on aligned text-audio pairs. It uses CLAP (Cross-Modal Latent Alignment Pretraining) to create a shared embedding space between both modalities. Essentially, CLAP teaches the model the intrinsic relationships between textual concepts and their associated acoustic patterns. AudioLDM taps into these co-embeddings, using the audio-side to represent its internal generative model and the text-side to provide conditional inputs that guide the speech output. This technique gives AudioLDM huge flexibility in synthesizing voices from free-form text prompts. Even for unusual text inputs, the cross-modal embeddings convey informative guidance rooted in the structures of natural speech. In the paper, for example, the creators demonstrate that AudioLDM can create sounds from very unusual text prompts, like "a wolf is singing a beautiful song." So by breaking conventions with unpaired learning, opening up greater possibilities through continuous latent modeling, and leveraging cross-modal conditioning, AudioLDM manages to unlock new horizons for controllable speech synthesis. Its technical inventiveness has opened exciting doors for creating more immersive, expressive conversational experiences. AudioLDM's strengths and weaknesses While AudioLDM demonstrates leading-edge achievements in controllable audio synthesis, it excels better at some tasks than others. Let's examine its capabilities and limitations for critical audio generation use cases. Diverse Audio Creation and Speech Synthesis AudioLDM shines at producing novel sound effects and music described in text prompts. Training on diverse datasets like AudioSet equips it to synthesize a wide range of sounds. Its continuous latent space enables smooth interpolations for generating musical notes and transitions. These traits make AudioLDM a versatile tool for audio creators. Intelligible but Imperfect Speech For speech synthesis, AudioLDM can generate intelligible voices from text and manipulate attributes like speaker identity based on prompts. It's capable of generating intelligible speech from text and can use attributes like the speaker's identity based on prompts. However, speech quality falls short of specialized . Subtleties like emphasis and cadence may not translate accurately, and artifacts are often audible. state-of-the-art TTS models Long-Form Audio Still a Challenge A current limitation is that AudioLDM works best for short clips of up to 10 seconds. Maintaining consistency over longer timescales is difficult as outputs are produced in small steps. Generating coherent long-form audio like podcasts or audiobooks remains an open research problem. You may wish to check into other models for these kinds of applications. auto-regressively The Nuances of Speech and Data Dependencies Despite its groundbreaking achievements, AudioLDM struggles with the complexity of human speech. While it can generate a diverse array of voices, it often fails to replicate the subtle emotional nuances and natural cadence inherent to human speech. This means elements like emphasis, sarcasm, and vocal richness are sometimes lost in translation. Further, AudioLDM's unpaired training scheme, although reducing the need for labeled data, remains a data-hungry beast. The model requires massive quantities of unlabeled audio data for pretraining, which may be beyond the reach of early-stage startups. Data partnerships could offer a solution, but come with their own set of legal and privacy issues. The complexities of AudioLDM extend beyond data needs. The model's advanced neural architecture demands significant computational resources for training, which can pose a challenge for startups with limited hardware capabilities. While cloud-based AI services could offset these demands, they would inevitably incur considerable costs. Precision Editing Limitations As a generative model, AudioLDM is designed for stochastic synthesis rather than precise audio editing. Operations like voice conversion or sound filtering require specialized techniques beyond their capabilities. If editing precision is needed, AudioLDM may not suffice in its current form. Black Box Outputs Like many neural networks, the internal workings of AudioLDM are opaque. This makes debugging quality issues and diagnosing unintended biases in the synthesized voices more difficult. Mechanisms to peek inside the black box and gain insight into failure modes would aid responsible development. By knowing about these limitations, you can make informed decisions about integrating AudioLDM into products, and direct research efforts towards mitigating the vulnerabilities. AI advances like AudioLDM inevitably bring new challenges alongside their benefits. Keeping grounded about their shortcomings is crucial to steer progress in the right direction. Comparing Apples to Apples: AudioLDM Vs. the Rest There are several AI models for generating audio from text prompts. Here we'll compare some of the popular options to AudioLDM to help determine the right fit for your needs. Riffusion uses stable diffusion to generate musical audio in real-time. It's useful for building things like interactive music apps. AudioLDM can also create music, but isn't specialized for real-time generation. Consider Riffusion if you need dynamic, improvisational music. Riffusion Musicgen converts text prompts into musical compositions. This makes it useful for assisting musicians, composers, or music students. AudioLDM has wider sound synthesis capabilities beyond just music. Musicgen may offer higher quality and more control for music specifically. Musicgen Bark generates realistic speech and sound effects from the text. The speech sounds more natural than AudioLDM and Bark is great to use if you're building voice assistants or audiobooks. The bark is focused on high-fidelity speech, while AudioLDM has a greater diversity of sound effects. Bark may be preferable for voice-centric applications. Bark Tortoise TTS converts text to natural-sounding speech. Like Bark, it's useful for voice assistants, audiobooks, and speech synthesis. Tortoise TTS specializes in human-like voices, but AudioLDM enables greater voice control and variation. Choose based on speech quality needs. Tortoise TTS Summary For specialized music generation, consider Riffusion or Musicgen. For voice-centric applications, Bark or Tortoise TTS may be ideal. AudioLDM provides versatile sound synthesis and voice control, but less focus on tailored domains. Here is a comparison table for the audio generation models, using data from . AIModels.fyi Model Creator Cost per Run Avg Run Time Hardware Use Cases Riffusion riffusion $0.0066 12 seconds Nvidia T4 GPU Real-time music generation, interactive music apps Musicgen joehoover $0.0943 41 seconds Nvidia A100 GPU Assisting musicians, composers, music students Bark suno-ai $0.0297 54 seconds Nvidia T4 GPU Voice assistants, audio books, sound effects Tortoise TTS afiaka87 - - Nvidia T4 GPU Voice assistants, audio books, speech synthesis Wrapping Up: Is AudioLDM the best model for your text-to-audio product? AudioLDM demonstrates cutting-edge innovation in controllable audio synthesis. Its novel techniques open up new creative possibilities that were once inconceivable. However, as with any new technology, there are tradeoffs to weigh before integrating it into products. Consider these key questions: Models like Riffusion, Musicgen, Bark, and Tortoise TTS may be better suited. Does your application require specialized music generation or exceptionally natural speech? If not, opt for lighter and more accessible alternatives. Can you accommodate AudioLDM's heavy data and compute requirements? AudioLDM is better for generative use cases than editing workflows. Is precision editing critical? AudioLDM's "black box" nature may pose challenges. Are model interpretability and transparency important? If not, you may be able to leverage a model hosting provider or platform like Replicate, but at an increased cost. Does your team have ML ops maturity to responsibly develop with AudioLDM? More specialized models could be a better fit. Are your end goals focused on niche audio domains? By weighing these key factors against the breakthrough capabilities AudioLDM provides, you can determine if now is the right time to pilot it in your products. With diligent evaluation, you can strategically harness AudioLDM's power to provide delightful user experiences and disrupt industries. Remember that AI models are improving all the time, and future advances may change key factors that could tip the scales in favor of implementing this model. Be sure to keep a like this one to see how they improve and change. Thanks for reading! close eye on models Subscribe or follow me on Twitter for more content like this! Resources and Further Reading You might find some of these other articles interesting or helpful in your quest to build your AI project. - Overview of the Whisper speech-to-text model. Converting Speech into Text with OpenAI's Whisper Model - Explains how AudioLDM synthesizes speech and sound from text. Audio LDM: AI Text to Audio Generation with Latent Diffusion Models - Guide to making your friends lip sync with AI models. Learn How to Harness the Power of AI for Lip Syncing Videos - Compares image upscaling models. Another technical dive similar to this one. Real-ESRGAN vs. Real-ESRGAN: From Theoretical to Real-World Super Resolution with AI Also published . here

A Creator's Guide to Synthesizing Sound Effects, Music, and Dialog With AudioLDM

First and Last Things by H. G. Wells, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . THE SYNTHESIS. THE SYNTHESIS. It seems to me that the whole living creation may be regarded as walking in its sleep, as walking in the sleep of instinct and individualized illusion, and that now out of it all rises man, beginning to perceive his larger self, his universal brotherhood and a collective synthetic purpose to increase Power and realize Beauty... I write this down. It is the form of my belief, and that unanalyzable something called Beauty is the light that falls upon that form. It is only by such images, it is only by the use of what are practically parables, that I can in any way express these things in my mind. These two things, I say, are the two aspects of my belief; one is the form and the other the light. The former places me as it were in a scheme, the latter illuminates and inspires me. I am a member in that great being, and my function is, I take it, to develop my capacity for beauty and convey the perception of it to my fellows, to gather and store experience and increase the racial consciousness. I hazard no whys nor wherefores. That is how I see things; that is how the universe, in response to my demand for a synthesizing aspect, presents itself to me. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. H. G. Wells (2009). First and Last Things: A Confession of Faith and Rule of Life. Urbana, Illinois: Project Gutenberg. Retrieved October 2022, https://www.gutenberg.org/files/4225/4225-h/4225-h.htm This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

THE SYNTHESIS

We’ve heard of , we’ve heard of NeRFs, and we’ve seen these kinds of applications allowing you to recreate someone’s face and pretty much make him or her say whatever you want. deepfakes What you might not know is how inefficient those methods are and how much computing and time they require. Plus, we only see the best results. Keep in mind that what we see online is the results associated with the faces we could find most examples of, so basically, internet personalities and the models producing those results are trained using lots of computing, meaning expensive resources like many graphics cards. Still, the results are really impressive and only getting better. Fortunately, some people like Jiaxian Tang and colleagues are working on making those methods more available and effective with a new model called RAD-NeRF. From a single video, they can synthesize the person talking for pretty much any word or sentence in real time with better quality. You can animate a talking head following any audio track in real-time. This is both so cool and so scary at the same time... Learn more in the video References ►Tang, J., Wang, K., Zhou, H., Chen, X., He, D., Hu, T., Liu, J., Zeng, G. and Wang, J., 2022. Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition. . ►Results/project page: ​​ arXiv preprint arXiv:2211.12368 https://me.kiui.moe/radnerf/ Video Transcript 0:02 [Music] 0:07 we've heard of deep fakes we've heard of 0:09 Nerfs and we've seen these kinds of 0:11 applications allowing you to recreate 0:13 someone's face and pretty much make him 0:15 say whatever you want what you might not 0:17 know is how inefficient those methods 0:20 are and how much Computing and time they 0:22 require plus we only see the best 0:24 results keep in mind that what we see 0:26 online is the results associated with 0:29 the faces we could find most examples of 0:31 so basically internet personalities and 0:34 the models producing those results are 0:36 trained using lots of computing meaning 0:38 expensive resources like many graphics 0:41 cards still the results are really 0:43 impressive and only getting better 0:45 fortunately some people like Jackson 0:47 tang and colleagues are working on 0:49 making those methods more available and 0:52 effective with a new model called red 0:54 Nerf but let's hear that from their own 0:57 model hello thanks for watching the 0:59 supplementary video for our paper 1:00 real-time neural Radiance talking head 1:03 synthesis via decomposed audio spatial 1:05 encoding 1:06 our method is person-specific and only 1:08 needs a three to five minutes monocular 1:10 video for training 1:11 after training the model can synthesize 1:14 realistic Talking Heads driven by 1:15 arbitrary audio in real time while 1:17 keeping comparable or better rendering 1:19 quality compared to previous methods so 1:21 you heard that right from a single video 1:23 they can synthesize the person talking 1:26 for pretty much any word or sentence in 1:28 real time with better quality you can 1:30 animate a talking head following any 1:33 audio track in real time this is both so 1:36 cool and so scary at the same time just 1:39 imagine what could be done if we could 1:40 make you say anything at least they 1:43 still need access to a video of you 1:45 speaking in front of the camera for 5 1:47 minutes so it's hard to achieve that 1:48 without you knowing still as soon as you 1:51 appear online anyone will be able to use 1:53 such a model and create infinite videos 1:56 of you talking about anything they want 1:58 they can even host live streams with 2:00 this method which is even more dangerous 2:03 and makes it even harder to say wetsuit 2:05 or not anyways even though this is 2:08 interesting and I'd love to hear your 2:10 thoughts in the comments and keep the 2:11 discussion question going here I wanted 2:13 to cover something that is only positive 2:15 and exciting science more precisely how 2:19 did they achieve to animate Talking 2:20 Heads in real time from any audio using 2:23 only a video of the face as they State 2:26 their red Nerf model can run 500 times 2:29 faster than the previous works with 2:31 better rendering quality and more 2:33 control you may ask how is that possible 2:36 we usually trade quality for efficiency 2:39 yet they achieve to improve both 2:41 incredibly these immense improvements 2:43 are possible thanks to three main points 2:46 the first two are related to the 2:48 architecture of the model more 2:50 specifically how they adapted the Nerf 2:52 approach to make it more efficient and 2:54 with improved motions of the Torso and 2:57 head the first step is to make nerves 2:59 more efficient I won't dive into how 3:02 Nerfs work since we covered it numerous 3:04 time basically it's an approach based on 3:06 neural networks to reconstruct 3D 3:09 volumetric scenes from a bunch of 2D in 3:11 images which means regular images this 3:14 is why they will take a video as input 3:17 as it basically gives you a lot of 3:19 images of a person from many different 3:21 angles so it usually uses a network to 3:24 predict all pixels colors and densities 3:26 from the camera Viewpoint you are 3:28 visualizing and does that for all 3:31 viewpoints you want to show when 3:32 rotating around the subject which is 3:34 extremely computation hungry as you are 3:37 predicting multiple parameters for each 3:39 coordinate in the image every time and 3:41 you are learning to predict all of them 3:43 Plus in their case it isn't only a Nerf 3:46 producing or 3D scene it also has to 3:49 match an audio input and fit the lips 3:51 mouth eyes and movements with what the 3:53 person says instead of predicting all 3:56 pixels densities and colors matching the 3:58 audio for a specific frame they will 4:00 work with two separate new and condensed 4:03 spaces called grid spaces or grid-based 4:06 Nerf they will translate their 4:08 coordinates into a smaller 3D grid space 4:11 trans laid their audio into a smaller 2D 4:13 grid space and then send them to render 4:16 the head this means they never merge the 4:19 audio data with the spatial data which 4:22 will increase the size exponentially 4:23 adding two dimensional inputs to each 4:26 coordinate so reducing the size of the 4:29 audio features along with keeping the 4:31 audio and spatial features separate is 4:34 what makes the approach so much more 4:36 efficient but how can the results be 4:38 better if they use condensed spaces that 4:40 have less information adding a few 4:42 controllable features like an eye 4:44 blinking control to our grid Nerf the 4:47 model will learn more realistic 4:48 behaviors for the eyes compared to 4:51 previous approaches something really 4:53 important for realism the second 4:55 Improvement they've done is to model the 4:57 Torso with another Nerf using the same 5:00 approach instead of trying to model it 5:02 with the same Nerf used further head 5:04 which will require much fewer parameters 5:07 and different needs as the goal here is 5:09 to animate moving heads and not whole 5:12 bodies since the Torso is pretty much 5:14 static in these cases they use a much 5:16 simpler and more efficient Nerf based 5:18 module that only works in 2D working in 5:21 the image space directly instead of 5:24 using camera arrays as we usually do 5:26 with Nerf to generate many different 5:28 angles which aren't needed for a torso 5:30 so it is basically much more efficient 5:32 because they modified the approach for 5:35 this very specific use case of the rigid 5:37 torso and moving head videos they then 5:40 recompose the head with the Torso to 5:42 produce the final video and voila this 5:45 is how you produce talking head videos 5:47 over any audio input super efficiently 5:50 of course this was just an overview of 5:53 this new exciting research publication 5:55 and they do other modifications during 5:57 the training of their algorithm to make 5:59 it more efficient which is the third 6:01 point I mentioned at the beginning of 6:03 the video if you were wondering I invite 6:05 you to read their paper for more 6:07 information the link is in the 6:09 description below before you leave I 6:10 just wanted to thank the people who 6:12 recently supported this channel through 6:14 patreon this is not necessary and 6:16 strictly to support the work I do here 6:18 huge thanks to artem vladiken Leopoldo 6:22 Alta Murano J Cole Michael carichao 6:25 Daniel gimness and a few Anonymous 6:28 generous donors it will be greatly 6:30 appreciated if you also want and can 6:33 afford to support my work financially 6:35 the link to my patreon page is in the 6:37 description below as well but no worries 6:39 if not a sincere comment below this 6:42 video is all I need to be happier I hope 6:45 you've enjoyed this video and I will see 6:47 you next week with another amazing paper 6:51 [Music]

Efficient NeRFs for Real-Time Portrait Synthesis (RAD-NeRF)

NeRF eficientes para la síntesis de retratos en tiempo real (RAD-NeRF)

रीयल-टाइम पोर्ट्रेट सिंथेसिस (आरएडी-एनईआरएफ) के लिए कुशल एनईआरएफ

リアルタイム ポートレート合成 (RAD-NeRF) のための効率的な NeRF

NeRFs eficientes para síntese de retrato em tempo real (RAD-NeRF)

NeRF hiệu quả để tổng hợp chân dung thời gian thực (RAD-NeRF)

用于实时人像合成的高效 NeRF (RAD-NeRF)

Stories, News & Mentions about SYNTHESIA

Synthesia’s Stories on HackerNoon

Synthesia's News & Mentions