I tested the new tool and, as someone who often edits and creates videos, I was impressed.

ElevenLab's AI sound effect generator has finally launched. Listen for yourself | ZDNET

<strong>ElevenLabs</strong> is rolling out a fine-tuned and ready-for-market version of its impressive generative AI sound effects engine for the first time.

I put ElevenLabs new AI sound effects generator to the test — its great for AI video | Tom's Guide

Voice cloning startup <strong>ElevenLabs</strong> introduced a new tool for users to generate sound effects through prompts today. The sound effects tool can also generate instrumental musical clips of up to 22 seconds with prompts such as guitar loops, jazz saxophone solos and music techno loops.

ElevenLabs debuts AI-powered tool to generate sound effects

ElevenLabs

elevenlabs

Meet the Indian Startup That’s Building a Global Entertainment Platform on the Back of AI, and How

Süni intellektin arxasında Qlobal Əyləncə Platforması quran Hindistan Startapı ilə tanış olun və necə

Γνωρίστε την ινδική startup που χτίζει μια παγκόσμια πλατφόρμα ψυχαγωγίας στο πίσω μέρος του AI και πώς

Conozca la startup india que está construyendo una plataforma de entretenimiento global con inteligencia artificial y cómo

मिलिए उस भारतीय स्टार्टअप से जो AI के दम पर बना रहा है ग्लोबल एंटरटेनमेंट प्लेटफॉर्म, जानिए कैसे

Rankontre demaraj Endyen an ki ap konstwi yon platfòm Divètisman mondyal sou do AI, ak kijan

AIを活用したグローバルエンターテイメントプラットフォームを構築するインドのスタートアップ企業とその方法

AI артында жаһандық ойын-сауық платформасын құратын үнділік стартаппен танысыңыз және қалай

AI аркасында глобалдык көңүл ачуу платформасын куруп жаткан индиялык стартап менен таанышыңыз жана кантип

Sangana neIndia Kutanga Iko Kuvaka Global Varaidzo Platform kuseri kweAI, uye Sei

La kulan Bilowga Hindida Kaas oo Dhisaya Madal Madadaalada Caalamiga ah ee Dhabarka AI, iyo Sidee

Yapay Zekanın Arkasında Küresel Bir Eğlence Platformu Kuran Hintli Girişimle Tanışın ve Bunu Nasıl Yapıyor?

Gặp gỡ công ty khởi nghiệp Ấn Độ đang xây dựng nền tảng giải trí toàn cầu dựa trên AI và cách thức

In this article, we examine the potential disruption of the radio host/live-stream industry through the use of artificial intelligence technology. By analyzing the average radio host salary, the number of spots per hour, the total words spoken per hour, and other factors such as music licensing fees, we demonstrate that it is possible to create a fully automated radio station using AI technology at a fraction of the cost of hiring human talent. We showcase a live demonstration of one such solution currently active under Avalon Star Streams brand, where after running songs from a creative commons music stream, the AI generates new radio content during breaks between those songs, even allowing the random selection of new songs if desired. Finally, we provide details on the tech stack utilized including NodeJS docker image which allows us to control ffmpeg streams and manage playlists efficiently. Running Example At the time of this writing, you can find the example live-streaming at and . Twitch.TV YouTube If for any reason the demo is not live streaming feel free to checkout this YouTube video for an example. https://youtu.be/Jnb4bMJc7x8?embedable=true the YouTube video show cases 2 auto-generated scripts with the AI Voice Actor Antoni Starr. The first is a call for donations, and the second is a random ad play with announcing the next song. NOTE: Cost analysis The key driver behind the potential disruption of the radio host industry lies in the significant reduction in labor costs. Utilizing current pricing models, the yearly cost of AI voice services required to run a full radio station amounts only to approximately $4100 compared to a national average salary of radio hosts estimated around $42k [0]. With an average radio spot duration of 16 minutes and an average speaking rate of 140 words per minute [1][2], each radio spot consists of approximately 2,240 words. AI-powered content generation, such as ChatGPT, comes at a cost of around $0.002 per 750 words. Thus, the total cost of using GPT for one hour of content amounts to approximately $0.006. With radio hosts spending around 45.5 hours per month on air [1], the yearly cost of GPT usage is estimated to be approximately $55. Furthermore, integrating Eleven Labs’ AI voice technology, priced at $330 per month for 40 hours of usage [3], incurs an annual cost of around $4,000. Considering the combined expenses of GPT and Eleven Labs, the total cost of implementing AI for a radio host is approximately $4,100 per year. This represents a significant cost reduction compared to traditional production methods and opens up new possibilities for radio/live-stream hosts with limited budgets. Further considerations While some might argue that AI generated content lacks emotional depth and personal touch provided by human talent, recent advancements in natural language processing have shown otherwise. With deep learning algorithms, AI systems can now analyze vast troves of linguistic data and learn nuances in context, tone and cadence of speech. When trained properly, these systems are able to mimic human-like qualities while still maintaining accuracy and efficiency. In fact, many industries ranging from customer service to journalism have already seen initial success with implementing chat-bots and machine-generated content due to economic advantages, even in sectors thought immune to technological takeover. Ultimately, it seems reasonable to assume a similar future for broadcasting markets like radio hosting. Examples Under our Avalon Star Stream brand, we set up a proof of concept showcasing the efficiency of AI assisted broadcasting. Leveraging open-source tools like ffmpeg and integrated within our custom NodeJS application framework managed through Docker, we were able to achieve a functioning automated live-stream setup complete with real-time generation capabilities for its intermission radio jockey. The system, under default settings, will play 3 songs before attempting a song-break. During the song-break our model analyzes prompts received online during the previous three musical sets and produces its own original written material targeting the donators thanking them, before reading an ad for an imaginary product and continuing. Our model is told take the persona of a Radio Host living within the by the name of “Antoni Starr”. Fallout 4 Universe Due to budget constraints, Antoni employs a cost-saving strategy. While generating content, there is a 10% chance, limited to once per hour, that his system dynamically pulls and generates a new song-break. This method adds an element of surprise and uniqueness to the show while optimizing production costs for the purpose of this tech demo. All other ad-reads will come from a previously generated grab-bag created during testing. Additionally, due to the channel being so new, we are unable to turn on the subscribers/memberships to be used during announcements. Tech Stack While I have not decided to release my code for this yet, I have decided to talk about the tech stack. As seen from the above image, the tool leveraging various technologies (FFmpeg, WebDAV, ChatGPT, EleventLabs, MongoDB) and the application combines them into a platform for live-stream generation. WebDAV + MongoDB This piece of the tech-stack is here to help record generated content and act as a file-store. The WebxDAV aspect allows us to remotely store the music files and download them at instantiation for the stream. ChatGPT + Eleven Labs These are the workhorses of the generative content. When time to generate a new ad-break we leverage ChatGPT API with our custom prompt to get the next script. Our prompt will be pre-seeded with name/information from stream donators and a random fake product to ad-read. FFmpeg The workhorse of streaming. FFmpeg is responsible for all audio/visual you see on the stream. from the static image overlay, to the encoded video playing on the TV and the audio you hear. FFmpeg is the magic behind it all. HTML/CSS/JavaScript Not seen in the diagram above, there is a management interface to tweak params of the running stream. This allows for the admin to force ad-breaks, tweak ad-break rate algorithm and more. Additionally, as mentioned previously, the entire platform is running off of NodeJS Conclusion We examined the possibility of replacing radio hosts with artificial intelligence and concluded that, given certain conditions, it may indeed be possible to do so. Our findings suggest that AI-powered radio stations would have clear financial advantages over their human counterparts and be capable of producing high-quality content equal to or surpassing that of human DJs. Further consideration should be done to ensure that listeners remain engaged and attuned to the program’s offerings amidst such developments. Overall, while the idea of a completely automated radio station or live-stream, with a dynamic voiced personality, may initially seem farfetched, the reality is that emerging technologies are quickly making the notion feasible and practical. Thus, business leaders must recognize the changing landscape and adapt accordingly before they risk being left behind in an ever evolving marketplace. Live-stream Links and Twitch.TV Youtube If you want to see more of tool itself please don’t hesitate to reach out.

The Radio Host and Live-Stream Industry: Poised for GPT Disruption

রেডিও হোস্ট এবং লাইভ-স্ট্রিম শিল্প: GPT ব্যাঘাতের জন্য প্রস্তুত

Die Radiomoderator- und Live-Stream-Branche: Bereit für GPT-Störungen

La industria de los presentadores de radio y la transmisión en vivo: preparados para la interrupción de GPT

L'industrie de l'hôte radio et de la diffusion en direct : prête pour la perturbation des GPT

रेडियो होस्ट और लाइव-स्ट्रीम उद्योग: GPT व्यवधान के लिए तैयार

ラジオホストとライブストリーム業界: GPT 破壊の準備が整っている

라디오 진행자와 라이브 스트리밍 산업: GPT 혼란에 대비

O apresentador de rádio e a indústria de transmissão ao vivo: preparados para a interrupção do GPT

Radyo Sunucusu ve Canlı Yayın Sektörü: GPT Kesintisine Hazır

Ngành công nghiệp máy chủ radio và phát trực tiếp: Đã sẵn sàng cho sự gián đoạn của GPT

电台主持人和直播行业：为 GPT 颠覆做好准备

For those of you who have been following me for a while, you know that I'm passionate about artificial intelligence (AI) and all the potential it holds. I've written several articles on it, ranging from to and . I'm constantly reading and experimenting in this domain. From small Python applications to help with article creation. ChatGPT Wordtune 174 AI Tools To Try Lately, I've noticed a lot of content about how to create an animated avatar using AI. So, I thought I'd give it a shot! Your Avatar To get your avatar made, use a drawing AI app like or DALL-E 2 Midjourney The image below was created with Midjourney: , you will require Discord. If you don't have an account, make one first. To register for Midjourney Then, click the Midjourney logo on the top left to access the 'newbie' channels. From there type ‘/imagine’ Proceed with the prompt. Here you can indicate what Midjourney should create for you. This is the prompt I used: Midjourney then asks you to accept the Terms of Service before giving you 4 images to choose from. You can edit those as well. I picked the one top-right. Pick the one that looks best to you and save it! Script for the Spoken Word I used ChatGPT to create a script. I entered a prompt and it delivered this text below. Now, this piece of text has to transform into a spoken word audio file. Spoken Word Fear not, as I've got the solution - audibly record my text? No way! I'm not a fan of hearing my own voice, so that's off the table. But don't worry, is here to save the day and make my life easier. ElevenLabs 333 or 10,000 If you don’t register with ElevenLabs, you will have 333 characters at your disposal to convert into speech. I registered, so I have 10,000 characters to convert into speech. As you can see, there's a bunch of voices to pick from. I chose the first one, and it read my text out loud. Copy what ChatGPT gave you into the text box at ElevenLabs and press that 'Generate' button. Once you do, you'll see a play icon on the bottom part of your screen. Just hit it, and you hear the text read out loud, and you can experiment with different voices! Have fun! If you're thrilled with the text and the accompanying vocalizations, click that download button! Then, there's just one more step to complete this tale - getting the animation. Create the Animation For this, I used . This application is very easy to use. D-ID In the left bar, click on ‘Create Video’ In the next screen, you will add your avatar, the one you created with Midjourney, as a Presenter. You do this by clicking ‘Add’ under the heading ‘Choose a presenter’ You choose the file you want to upload and you’re done with the presenter. We’ll keep on clicking. On the right side of your screen, you’ll see the option to upload Voice Audio. Click on ‘Upload your own voice’ and load the audio file you saved at the ElevenLabs step earlier. If that went well, you can click on ‘Generate Video’ at the top right. It takes some time to generate your video. Your Own Animated AI Avatar https://youtu.be/b6fIbN2Ao4I?embedable=true Sources (& Tips) How to Make an AI Animated Avatar A Complete Tutorial Create Your Own AI Animated Avatar: A Step-by-Step Guide Bring Your MidJourney AI Avatar To Life (AI Animation) How to Create Your Own AI Animated Avatar How to Create AI Avatar with AI: A Step-by-Step Guide Create Your Own AI Avatar in Under 5 Min Also Published Here

Your Own Animated AI Avatar

Are you looking to create engaging faceless short videos for platforms like YouTube or TikTok but want to avoid the hassle of complex video editing? This article will walk you through how to automate the entire process using OpenAI, ElevenLabs, and MoviePy. By the end of this tutorial, you'll know how to automatically generate visuals and voiceovers for short videos based on any script. Whether you’re creating educational content, storytelling, or meme videos, this workflow will save you tons of time. Prerequisites Before getting started, you’ll need: API keys for both OpenAI (for generating visuals) and ElevenLabs (for voiceovers).
Basic Python knowledge.
MoviePy and other required Python libraries installed (moviepy, openai, elevenlabs, etc.). Step 1: Setting Up API Keys import openai
from elevenlabs import ElevenLabs

# Set up your OpenAI and ElevenLabs API keys
openai.api_key = "your_openai_api_key"
elevenlabs_client = ElevenLabs(api_key="your_elevenlabs_api_key") Start by getting API keys from OpenAI and ElevenLabs. Replace the placeholders in the code with your actual API keys. Step 2: Preparing the Script Your video starts with a story or script. You can replace the story_script variable with the text you want to turn into a video. Here’s an example script about Dogecoin: story_script = """
Dogecoin began as a joke in 2013, inspired by the popular 'Doge' meme featuring a Shiba Inu dog. It unexpectedly gained a massive following thanks to its community's charitable initiatives, eventually evolving into a legitimate cryptocurrency with support from Elon Musk.
""" The script will be split into sentences to match each visual and audio segment. Step 3: Generating Images with OpenAI’s DALL-E For each sentence, we generate a corresponding image using OpenAI’s DALL-E model. def generate_image_from_text(sentence, context, idx):
    prompt = f"Generate an image without any text that describes: {sentence}. Context: {context}"
    response = openai.images.generate(
        model="dall-e-3",
        prompt=prompt,
        size="1024x1792",
        response_format="b64_json"
    )

    image_filename = f"images/image_{idx}.jpg"
    with open(image_filename, "wb") as f:
        f.write(base64.b64decode(response.data[0].b64_json))
    return image_filename This function sends each sentence to DALL-E and saves the generated image. We ensure the generated visuals match the video's theme. Step 4: Generating Voiceovers with ElevenLabs Once we have the visuals, we need voiceovers. ElevenLabs converts each sentence into speech. def generate_audio_from_text(sentence, idx):
    audio = elevenlabs_client.text_to_speech.convert(
        voice_id="pqHfZKP75CvOlQylNhV4",
        model_id="eleven_multilingual_v2",
        text=sentence,
        voice_settings=VoiceSettings(stability=0.2, similarity_boost=0.8)
    )
    audio_filename = f"audio/audio_{idx}.mp3"
    with open(audio_filename, "wb") as f:
        for chunk in audio:
            f.write(chunk)
    return audio_filename This function generates an audio file for each sentence. You can select different voice settings to customize the narration style. Step 5: Combining Audio and Video Next, we pair each image with its corresponding voiceover using MoviePy: from moviepy.editor import ImageClip, AudioFileClip

image_clip = ImageClip(image_path, duration=audio_clip.duration)
image_clip = image_clip.set_audio(audio_clip)
video_clips.append(image_clip.set_fps(30)) Each image is displayed for the duration of its audio clip, ensuring synchronization. Step 6: Applying Video Effects To make the video more dynamic, we apply zoom and fade effects to each image. For example, the apply_zoom_in_center effect slowly zooms into the center of the image: def apply_zoom_in_center(image_clip, duration):
    return image_clip.resize(lambda t: 1 + 0.04 * t) Other effects include zooming in from the upper part or zooming out. These effects are applied randomly to each clip to keep the video visually engaging. Step 7: Final Video Assembly We combine all video clips into one seamless video and add background music: final_video = concatenate_videoclips(video_clips, method="compose")
final_video.write_videofile(output_video_path, codec="libx264", audio_codec="aac", fps=30) Step 8: Adding Captions Captions improve video accessibility and engagement. We use Captacity to automatically add captions based on the audio. captacity.add_captions(
    video_file=output_video_path,
    output_file="captioned_video.mp4",
    font_size=130,
    font_color="yellow",
    stroke_width=3
) Step 9: Adding Background Music To finish the video, background music is added. The volume is reduced so that it doesn't overpower the narration. background_music = AudioFileClip(music_filename).subclip(0, final_video.duration).volumex(0.2)
narration_audio = final_video.audio.volumex(1.5)
combined_audio = CompositeAudioClip([narration_audio, background_music])
final_video.set_audio(combined_audio) Conclusion The GitHub repository for this project is available here. By using OpenAI and ElevenLabs, we’ve automated the creation of faceless videos from text. You can now quickly generate YouTube Shorts or TikToks without needing a camera or microphone. This automated process has allowed us to create a Faceless Shorts Video service on our Robopost software, offering content creators a seamless way to produce high-quality videos. Whether you are creating educational videos, short stories, or even meme-style content, this service handles everything from visuals to voiceovers with minimal effort. Now, you can focus on creativity and storytelling while Robopost handles the heavy lifting of video production. Happy creating! Are you looking to create engaging faceless short videos for platforms like YouTube or TikTok but want to avoid the hassle of complex video editing? This article will walk you through how to automate the entire process using OpenAI , ElevenLabs , and MoviePy . OpenAI ElevenLabs MoviePy By the end of this tutorial, you'll know how to automatically generate visuals and voiceovers for short videos based on any script. Whether you’re creating educational content, storytelling, or meme videos, this workflow will save you tons of time. Prerequisites Before getting started, you’ll need: API keys for both OpenAI (for generating visuals) and ElevenLabs (for voiceovers). Basic Python knowledge. MoviePy and other required Python libraries installed (moviepy, openai, elevenlabs, etc.). API keys for both OpenAI (for generating visuals) and ElevenLabs (for voiceovers). API keys Basic Python knowledge. MoviePy and other required Python libraries installed ( moviepy , openai , elevenlabs , etc.). moviepy openai elevenlabs Step 1: Setting Up API Keys import openai
from elevenlabs import ElevenLabs

# Set up your OpenAI and ElevenLabs API keys
openai.api_key = "your_openai_api_key"
elevenlabs_client = ElevenLabs(api_key="your_elevenlabs_api_key") import openai
from elevenlabs import ElevenLabs

# Set up your OpenAI and ElevenLabs API keys
openai.api_key = "your_openai_api_key"
elevenlabs_client = ElevenLabs(api_key="your_elevenlabs_api_key") Start by getting API keys from OpenAI and ElevenLabs . Replace the placeholders in the code with your actual API keys. OpenAI ElevenLabs Step 2: Preparing the Script Your video starts with a story or script. You can replace the story_script variable with the text you want to turn into a video. Here’s an example script about Dogecoin : story_script Dogecoin story_script = """
Dogecoin began as a joke in 2013, inspired by the popular 'Doge' meme featuring a Shiba Inu dog. It unexpectedly gained a massive following thanks to its community's charitable initiatives, eventually evolving into a legitimate cryptocurrency with support from Elon Musk.
""" story_script = """
Dogecoin began as a joke in 2013, inspired by the popular 'Doge' meme featuring a Shiba Inu dog. It unexpectedly gained a massive following thanks to its community's charitable initiatives, eventually evolving into a legitimate cryptocurrency with support from Elon Musk.
""" The script will be split into sentences to match each visual and audio segment. Step 3: Generating Images with OpenAI’s DALL-E For each sentence, we generate a corresponding image using OpenAI’s DALL-E model. def generate_image_from_text(sentence, context, idx):
    prompt = f"Generate an image without any text that describes: {sentence}. Context: {context}"
    response = openai.images.generate(
        model="dall-e-3",
        prompt=prompt,
        size="1024x1792",
        response_format="b64_json"
    )

    image_filename = f"images/image_{idx}.jpg"
    with open(image_filename, "wb") as f:
        f.write(base64.b64decode(response.data[0].b64_json))
    return image_filename def generate_image_from_text(sentence, context, idx):
    prompt = f"Generate an image without any text that describes: {sentence}. Context: {context}"
    response = openai.images.generate(
        model="dall-e-3",
        prompt=prompt,
        size="1024x1792",
        response_format="b64_json"
    )

    image_filename = f"images/image_{idx}.jpg"
    with open(image_filename, "wb") as f:
        f.write(base64.b64decode(response.data[0].b64_json))
    return image_filename This function sends each sentence to DALL-E and saves the generated image. We ensure the generated visuals match the video's theme. Step 4: Generating Voiceovers with ElevenLabs Once we have the visuals, we need voiceovers. ElevenLabs converts each sentence into speech. def generate_audio_from_text(sentence, idx):
    audio = elevenlabs_client.text_to_speech.convert(
        voice_id="pqHfZKP75CvOlQylNhV4",
        model_id="eleven_multilingual_v2",
        text=sentence,
        voice_settings=VoiceSettings(stability=0.2, similarity_boost=0.8)
    )
    audio_filename = f"audio/audio_{idx}.mp3"
    with open(audio_filename, "wb") as f:
        for chunk in audio:
            f.write(chunk)
    return audio_filename def generate_audio_from_text(sentence, idx):
    audio = elevenlabs_client.text_to_speech.convert(
        voice_id="pqHfZKP75CvOlQylNhV4",
        model_id="eleven_multilingual_v2",
        text=sentence,
        voice_settings=VoiceSettings(stability=0.2, similarity_boost=0.8)
    )
    audio_filename = f"audio/audio_{idx}.mp3"
    with open(audio_filename, "wb") as f:
        for chunk in audio:
            f.write(chunk)
    return audio_filename This function generates an audio file for each sentence. You can select different voice settings to customize the narration style. Step 5: Combining Audio and Video Next, we pair each image with its corresponding voiceover using MoviePy : MoviePy from moviepy.editor import ImageClip, AudioFileClip

image_clip = ImageClip(image_path, duration=audio_clip.duration)
image_clip = image_clip.set_audio(audio_clip)
video_clips.append(image_clip.set_fps(30)) from moviepy.editor import ImageClip, AudioFileClip

image_clip = ImageClip(image_path, duration=audio_clip.duration)
image_clip = image_clip.set_audio(audio_clip)
video_clips.append(image_clip.set_fps(30)) Each image is displayed for the duration of its audio clip, ensuring synchronization. Step 6: Applying Video Effects To make the video more dynamic, we apply zoom and fade effects to each image. For example, the apply_zoom_in_center effect slowly zooms into the center of the image: apply_zoom_in_center def apply_zoom_in_center(image_clip, duration):
    return image_clip.resize(lambda t: 1 + 0.04 * t) def apply_zoom_in_center(image_clip, duration):
    return image_clip.resize(lambda t: 1 + 0.04 * t) Other effects include zooming in from the upper part or zooming out. These effects are applied randomly to each clip to keep the video visually engaging. Step 7: Final Video Assembly We combine all video clips into one seamless video and add background music: final_video = concatenate_videoclips(video_clips, method="compose")
final_video.write_videofile(output_video_path, codec="libx264", audio_codec="aac", fps=30) final_video = concatenate_videoclips(video_clips, method="compose")
final_video.write_videofile(output_video_path, codec="libx264", audio_codec="aac", fps=30) Step 8: Adding Captions Captions improve video accessibility and engagement. We use Captacity to automatically add captions based on the audio. Captacity captacity.add_captions(
    video_file=output_video_path,
    output_file="captioned_video.mp4",
    font_size=130,
    font_color="yellow",
    stroke_width=3
) captacity.add_captions(
    video_file=output_video_path,
    output_file="captioned_video.mp4",
    font_size=130,
    font_color="yellow",
    stroke_width=3
) Step 9: Adding Background Music To finish the video, background music is added. The volume is reduced so that it doesn't overpower the narration. background_music = AudioFileClip(music_filename).subclip(0, final_video.duration).volumex(0.2)
narration_audio = final_video.audio.volumex(1.5)
combined_audio = CompositeAudioClip([narration_audio, background_music])
final_video.set_audio(combined_audio) background_music = AudioFileClip(music_filename).subclip(0, final_video.duration).volumex(0.2)
narration_audio = final_video.audio.volumex(1.5)
combined_audio = CompositeAudioClip([narration_audio, background_music])
final_video.set_audio(combined_audio) Conclusion The GitHub repository for this project is available here. The GitHub repository for this project is available here. By using OpenAI and ElevenLabs, we’ve automated the creation of faceless videos from text. You can now quickly generate YouTube Shorts or TikToks without needing a camera or microphone. This automated process has allowed us to create a Faceless Shorts Video service on our Robopost software , offering content creators a seamless way to produce high-quality videos. Whether you are creating educational videos, short stories, or even meme-style content, this service handles everything from visuals to voiceovers with minimal effort. Faceless Shorts Video service Faceless Shorts Video service on our Robopost software Robopost software Now, you can focus on creativity and storytelling while Robopost handles the heavy lifting of video production. Happy creating!

How to Use ElevenLabs and OpenAI to Automate Faceless Shorts Video for YouTube and TikTok

Do you know what generative voices are capable of? What vast possibilities does they open up, and how many areas can benefit from them? Maybe you need an app for voicing a video blog or book. Perhaps you want to bring a game character to life with a professional voiceover. How about creating an application for learning foreign languages? Today, has prepared a tutorial for you that will help you get to know AI voice technologies better! lablab.ai Let’s dive in! Introduction It  is one of the most exciting times for software development, what with the emergence of various " " tools in the market. Just name it, cover letter generation? Check! E-mail generation? Check! Automatic code comment generation? Check! Even outside coding and software development, the use case possibilities are enormous. generative AI Now, we can generate images with text prompts with various image generation models. Thus, it makes it possible for us to incorporate generated assets in our various products. The next question is: how about voices? The trend of user experiences in the past few years mentioned "voice command" as one of the emerging trend. It is only natural that the software we build will incorporate voices as one of the features. Which is why, in this tutorial, we will showcase the " " feature offered by in a simple app, which generates random words and have it spell it. To build the UI for this , we will use , a new UI library to share data science projects. Speech Synthesis ElevenLabs Python-based app Streamlit Introduction to ElevenLabs is a voice technology research company which offers solution. With easy to use , it allows developers to generate high-quality speeches using AI. It is made possible by the AI model which has been trained on a vast amount of audiobooks and also podcasts. The training allows the AI to deliver predictable and high-quality results in speech generation. ElevenLabs speech synthesis API There are two main features that has to offer, the first one is VoiceLab, where users can clone voices from recorded audio and/or existing pre-made voices, and also "design" voices based on gender, ages, ethnicities and races. Once users have the voices to work with, they can move on to the next feature, Speech Synthesis, where they can generate speeches using their designed voices or just using the pre-made ones. ElevenLabs Introduction to Model Anthropic's Claude is the latest AI model developed by , an AI research organization which focuses on improving the interoperability, robustness and safety of artificial intelligence systems. Claude Anthropic The model is designed to generate human-like responses, making it a powerful tool for a wide range of applications, from content creation, legal, to customer service. Just like any other in the market, is also trained on a diverse range of internet text. However, unlike most AI models, it has focus on "safety", which makes it possible to refuse outputs that it considers "harmful" or "untruthful" for the users. Claude AI models Claude Introduction to Streamlit is an open-source library that makes it easy and possible for developers and data scientists to create and share visually appealing and customized web apps. Developers can use to build and deploy fully featured data science apps in minutes. It is made possible by the simple and intuitive API that can be used to turn data scripts into UI components. Streamlit Python Streamlit Prerequisites Basic knowledge of and using Python UI development Streamlit Access to Anthropic API Access to ElevenLabs API Outline Initializing our Streamlit Project Adding Word Generation Feature using Model Claude Adding Speech Generation Feature using ElevenLabs API Testing the Word Generator App Discussion There are at least four steps that we will get through in this tutorial. First we need to initialize the Streamlit-based project, to get a general feel of developing user interfaces using Streamlit. Next, we start adding more features, beginning with engineering prompt to get model to give us a randomized word that is commonly misspelled. After that, we'll add text-to-voice generation provided by to demonstrate how the multilingual model spell the words. Finally, we're going to test the simple app. Claude ElevenLabs Initializing our Streamlit Project Let's get into the coding action! First, let's make a directory for our project and enter it! mkdir randomwords
cd randomwords Next, we're going to use this directory as the basis of our project. Because a project is essentially a project, we need to do some steps to initialize our project, such as defining and activating our virtual environment. Streamlit Streamlit Python Python # Creating the virtual environment
python -m venv env

# Activate the virtual environment
# On Linux/Mac
source env/bin/activate

# On Windows:
.\env\Scripts\activate Once activated, the output of our terminal should show the name of the virtual environment (env), like so: Next, it's time to install the libraries we need for this project! let's use the command to install the , , and library. Note that we also install a version-locked library to prevent a Pydantic-related error in one of the function. pip streamlit anthropic elevenlabs pydantic elevenlabs pip install streamlit anthropic elevenlabs "pydantic==1.*" With all the project's requirements out of the way, now let's dive into the coding part! Create a new file inside our project directory, let's call it . randomwords_app.py touch randomwords_app.py After the file is created, let's open the file in our favorite code editor or integrated development environment (IDE). For the starter, let's build our simple app from the simplest parts possible, a title and a text for the caption! import streamlit as st

st.title("Random Words Generator")

st.text("Hello, this is a random words generator app") To wrap up our project initialization part, let's try test running the app. Make sure that our current working directory is still inside our project and our virtual environment is already activated. When everything is ready, use the to run the app. streamlit run <app-name> streamlit run randomwords_app.py The app should open automatically in our default browsers! it should show the title and text for now. Next, we're going to add random word generation feature using model. Anthropic's Claude One last thing though, we'll have to provide our app with the API keys for the services that we're going to use, namely model and feature. As these keys are considered sensitive, we should keep them in a safe and isolated place. Anthropic's Claude ElevenLabs' Speech Synthesis However, this time we don't store them in a file. This is because Streamlit deal with environment variables differently. According to the , we need to create a secret configuration file inside a directory. We can create the directory inside our project and then create the file. .env documentation .streamlit mkdir .streamlit
touch .streamlit/secrets.toml Let's edit the TOML file we created, note that TOML file uses different formatting from the usual file. .env xi_api_key = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
claude_key = "sk-ant-xxxxxxxxxxxxxxxxxxxxxxxxxxxxx" Adding Word Generation Feature using Claude Model In this step, we will add a button that will generate the random word, the heading element to show the generated word and the subheading to show the meaning of the word. However, coming from a webdev background, I strongly believe that UI elements should be placed and arranged inside containers. So, we'll do exactly that. Import the necessary libraries First of all, let's add some import statements. We're going to import the library to generate our random words. anthropic import streamlit as st
import anthropic Then, before we get to the UI part, let's create our word generation function first. Defining the word generation function def generate_word():
    prompt = (f"{anthropic.HUMAN_PROMPT} Give me one non-English word that's commonly misspelled and the meaning. Please strictly follow the format! example: Word: Schadenfreude; Meaning: joy at other's expenses."
              f"{anthropic.AI_PROMPT} Word: Karaoke; Meaning: a form of entertainment where people sing popular songs over pre-recorded backing tracks."
              f"{anthropic.HUMAN_PROMPT} Great! just like that. Remember, only respond following the pattern.")

    c = anthropic.Anthropic(api_key=st.secrets["claude_key"])
    resp = c.completions.create(
        prompt=f"{prompt} {anthropic.AI_PROMPT}",
        stop_sequences=[anthropic.HUMAN_PROMPT],
        model="claude-v1.3-100k",
        max_tokens_to_sample=900,
    )

    print(resp.completion)
    return resp.completion In this function, the most heavy lifting is done by ** **l (Thanks, ! 😉). However, our part in this function is how to make Claude return the exact format consistently. So we need to both instruct to "strictly follow the format" and give it an example response by adding it after our initial prompt. Anthropic's Claude mode Claude Claude Finally, we make sure that Claude comply with our agreements by ask it to "Remember to only respond following the pattern". The function ends by returning the response from . Claude Next, let's get back to editing the UI! Updating the UI st.title("Random Words Generator")

with st.container():
    st.header("Random Word")
    random_word = st.subheader("-")
    word_meaning = st.text("Meaning: -")

    st.write("Click the `Generate` button to generate new word")
    if st.button("Generate"):
        result = generate_word()
        # Split the string on the semicolon
        split_string = result.split(";")

        # Split the first part on ": " to get the word
        word = split_string[0].split(": ")[1]

        # Split the second part on ": " to get the meaning
        meaning = split_string[1].split(": ")[1]

        print(f"word result: {word}")
        random_word.subheader(word)
        word_meaning.text(f"Meaning: {meaning}") This time, we added a container with some elements inside it. The header, subheader for displaying the random word, and the text element to show the meaning of the word. We also have a text to show the hint on how to use the app, as well as a button beneath it. In , we can declare click event handler by using a conditional statement, where it returns when the button is clicked. In this code, we invoke the function which returns the generated word and the meaning, and split the result into separate variables for the word and the meaning, respectively. Finally, we update the subheader and the text element to display the word and the meaning. Streamlit True generate_word() Final form Let's double check our code once again! It should contains the import statements, the function for generating the random word, and the updated UI which contains subheader, and text elements as well as button that generate the word by invoking the function. generate_word() import streamlit as st
import anthropic

def generate_word():
    prompt = (f"{anthropic.HUMAN_PROMPT} Give me one non-English word that's commonly misspelled and the meaning. Please strictly follow the format! example: Word: Schadenfreude; Meaning: joy at other's expenses."
              f"{anthropic.AI_PROMPT} Word: Karaoke; Meaning: a form of entertainment where people sing popular songs over pre-recorded backing tracks."
              f"{anthropic.HUMAN_PROMPT} Great! just like that. Remember, only respond following the pattern.")

    c = anthropic.Anthropic(api_key=st.secrets["claude_key"])
    resp = c.completions.create(
        prompt=f"{prompt} {anthropic.AI_PROMPT}",
        stop_sequences=[anthropic.HUMAN_PROMPT],
        model="claude-v1.3-100k",
        max_tokens_to_sample=900,
    )

    print(resp.completion)
    return resp.completion


st.title("Random Words Generator")

with st.container():
    st.header("Random Word")
    random_word = st.subheader("-")
    word_meaning = st.text("Meaning: -")

    st.write("Click the `Generate` button to generate new word")
    if st.button("Generate"):
        result = generate_word()
        # Split the string on the semicolon
        split_string = result.split(";")

        # Split the first part on ": " to get the word
        word = split_string[0].split(": ")[1]

        # Split the second part on ": " to get the meaning
        meaning = split_string[1].split(": ")[1]

        print(f"word result: {word}")
        random_word.subheader(word)
        word_meaning.text(f"Meaning: {meaning}") Testing the Word Generation Function Let's run the app once again with the same command. We can also just rerun the app by clicking the upper right menu and click " " if we've had the app running before. Rerun It should show this updated user interface. Now, let's try clicking the button! Generate One of the sweet things about Streamlit is that it handled loading and provided the loading indicator out of the box. We should see the indicator in the upper-right corner, as well as the option to " " the operation. Neat, huh? stop After a few seconds, the result should be showed in the UI. Perfect! notice that the app correctly split the generated text from the model into word and the meaning. However, if the result doesn't come out according to the expected format, we can always click the button again. Claude Generate The next step is the main feature of this app, to incorporate speech generation into our random word generator. Besides demonstrating how to generate the audio file using the API provided by ElevenLabs, this step also serve to demonstrate the capabilities of ElevenLabs' multilingual model. Adding Speech Generation Feature using API ElevenLabs The first step of this section is, as you've probably guessed, is to add more import statement! So, let's add some functions from that we'll use for the speech generation feature. elevenlabs import streamlit as st
import anthropic
++ from elevenlabs import generate, set_api_key Next, we're going to define the function to handle the speech generation. def generate_speech(word):
    set_api_key(st.secrets['xi_api_key'])
    audio = generate(
        text=word,
        voice="Bella",
        model='eleven_multilingual_v1'
    )

    return audio Thanks to the simplicity and readability of , and also easy-to-use API, we can generate the speech by using this code alone! The function accepts the random word which we use to generate the speech. We also specifically use "eleven_multilingual_v1" model which is a multilingual model, perfect for our use case to demonstrate the spelling and pronounciation of foreign and commonly misspelled words! Finally, we use the " " voice for this tutorial, which is one of the pre-made voice provided by . Python ElevenLabs Bella ElevenLabs Next, we'll add an audio player to play the generated speech. print(f"word result: {word}")
    random_word.subheader(word)
    word_meaning.text(f"Meaning: {meaning}")
++     speech = generate_speech(word)
++     st.audio(speech, format='audio/mpeg') Just below our latest code from earlier, we add the variable to store the generated speech, and run the speech using audio player provided by function from Streamlit. At this point, our app should work as expected, only showing the audio player when there is a random word available to "read". st.audio Curious how it works out? me too! let's test the app now! Testing the Word Spelling Feature Let's run the app again using or just rerun the app if we have it running already. It should look exactly the same as the last time we left it. However, let's try to click the "Generate" button this time! streamlit run Amazing! this time, the app also shows an audio player! Let's try playing it. Using the multilingual model, the speech generated should use the accent and intonation which is close to the origin language of the word. For example, "entrepreneur" should be pronounced in French accent. Conclusion In this short tutorial, hopefully we've explored the capabilities of speech generation technology offered by . With the multilingual model, it's easy to generate speeches that is intended for non-English listener. In our use case, we need multilingual model to aid us in finding the correct way to pronounce and spell non-English words that are commonly misspelled. ElevenLabs With so many ideas, we invite developers to join us in creating the future! , in where you can create your own voice AI app with models! (Additionally, you can leverage other AI models such as large language models, image and video generative models, etc., as long as they are not in direct competition with the hackathon technology). On July 28 lablab.ai launching a challenge ElevenLabs *Your final submission should consist of a ready-to-play working prototype of your idea, a video pitch, and a presentation showcasing your solution. You can find more tutorials and you can other hackathons to build with cutting-edge technologies! HERE JOIN And big thanks to the - the Author of this article. 💚 Septian Adi Nugraha

Building a Simple Word Spelling App with ElevenLabs, Streamlit, and Claude

ElevenLabs, Streamlit, এবং Claude সহ একটি সহজ শব্দ বানান অ্যাপ তৈরি করা

Erstellen einer einfachen Wortbuchstabier-App mit ElevenLabs, Streamlit und Claude

Creación de una aplicación de ortografía de palabras simple con ElevenLabs, Streamlit y Claude

Créer une application d'orthographe simple avec ElevenLabs, Streamlit et Claude

इलेवनलैब्स, स्ट्रीमलिट और क्लाउड के साथ एक सरल शब्द वर्तनी ऐप बनाना

イレブンラボ、Streamlit、Claude を使用してシンプルな単語スペル アプリを構築する

ElevenLabs, Streamlit 및 Claude를 사용하여 간단한 단어 맞춤법 앱 구축

Construindo um aplicativo simples de ortografia de palavras com ElevenLabs, Streamlit e Claude

ElevenLabs, Streamlit ve Claude ile Basit Bir Kelime Yazımı Uygulaması Oluşturma

Xây dựng ứng dụng đánh vần từ đơn giản với ElevenLabs, Streamlit và Claude

使用 ElevenLabs、Streamlit 和 Claude 构建简单的单词拼写应用程序

Get Rich Building Verticalized AI Wrappers (Even If You Don’t Code)

রিচ বিল্ডিং ভার্টিক্যালাইজড এআই র‍্যাপার পান (এমনকি যদি আপনি কোড না করেন)

Werden Sie reich durch die Entwicklung vertikaler KI-Wrapper (auch wenn Sie nicht programmieren können)

Hazte rico creando contenedores de IA verticalizados (aunque no sepas programar)

Devenez riche en créant des wrappers d’IA verticalisés (même si vous ne codez pas)

वर्टिकलाइज़्ड AI रैपर बनाकर अमीर बनें (भले ही आप कोड न जानते हों)

Menjadi Kaya dengan Membangun Pembungkus AI Vertikal (Bahkan Jika Anda Tidak Membuat Kode)

垂直統合型 AI ラッパーを構築して金持ちになる (コードを書かなくても)

Rich Building вертикалдандырылған AI орауыштарын алыңыз (тіпті кодтамасаңыз да)

수직화된 AI 래퍼를 구축하여 부자가 되세요(코딩하지 않아도)

Баян барилгын босоо AI боодолтой бол (та код бичээгүй байсан ч)

Fique rico construindo wrappers de IA verticalizados (mesmo que você não codifique)

Разбогатейте, создавая вертикальные оболочки ИИ (даже если вы не пишете код)

Hel dhismo qani ah oo toosan AI ku duub (Xitaa Haddi aadan Codayn)

Get Rich Building Verticalized AI Wrappers (Kahit Hindi Ka Mag-code)

Dikey AI Sarmalayıcıları Oluşturarak Zengin Olun (Kodlamasanız Bile)

رچ بلڈنگ ورٹیکلائزڈ AI ریپر حاصل کریں (چاہے آپ کوڈ نہ بھی کریں)

Làm giàu bằng cách xây dựng các trình bao bọc AI theo chiều dọc (ngay cả khi bạn không viết mã)

通过构建垂直化 AI 包装器致富（即使你不会编码）

London remains the number-one tech city outside of the US, based on Dealroom’s analysis that used a combination of factors including funding, patents, and billion-dollar-plus exits. The breadth of the activity can be noted during London Tech Week, an annual five-day event that brings together international enterprise leaders, government officials, and a dynamic expo to thousands of attendees. During the 2024 edition, held in the London Olympia in June, the hype around artificial intelligence (AI) in the UK’s capital could be heard loud and clear. In a strategically timely move, Salesforce unveiled the opening of its first dedicated AI Center on the eve of London Tech Week, coinciding with the company’s own London World Tour. Salesforce’s new AI Center will see a 40,000-square-foot facility opened up in the Blue Fin Building, an iconic landmark based on the south bank of the River Thames in Southwark, and will host a range of AI training and up-skilling programs while also building important connections between tech companies, specialists and Salesforce partners. But why did Salesforce opt for London, and was this decision a smart move? A vote of confidence for London Although Salesforce looks likely to open more AI Centers in key international locations in the future, its decision to opt for London as the first host city for this major endeavor speaks volumes for the potential of the city. Further, it looks like Salesforce isn’t the only tech firm that views London in a positive light with AI development. Research by London & Partners, one of the driving forces behind London Tech Week, found that some 77% of decision-makers at major international AI firms consider the city to be “more supportive of AI development than other major AI hubs,” according to Managing Director Janet Coyle. It’s also interesting to note that the AI Center isn’t the first play that Salesforce has made here. The company has committed $4 billion to invest in AI innovation over the next five years under Salesforce Ventures. To date, $200 million has already been awarded to UK AI firms including Eleven Labs, a provider of an AI-powered text-to-speech and voice generator, and the procurement platform AutoGen AI to name a couple of examples. The presence of the Salesforce Ventures fund coupled with the AI center shows a clear vote of confidence for London’s future in AI, but its success is likely to hinge on the wider tech ecosystem in the capital. The rise of AI centers in Europe However, it seems that London will be one of many cities hosting dedicated AI centers soon. For instance, Microsoft plans to invest $3.2 billion in AI facilities in Sweden, its largest-ever infrastructure bet in the Nordic country. The enterprise Data4 also announced this month plans to develop an AI center outside of Athens, signaling a boost to Greece’s digital infrastructure and economy. To understand more about the benefits of AI centers we spoke to Ranjit Tinaikar, CEO of Ness Digital Engineering (Ness), which earlier this year launched an innovation center in Europe. Speaking on the growth of AI centers globally and the opening of Salesforce’s inaugural AI Center in London, Tinaikar explained, “I think AI Centers are a very valid idea. And I completely understand the goals of becoming the R&D hub for driving AI innovation.” At the same time, he stressed the importance of the ecosystem’s foundation for determining the success of such programs. “If you were to compare Boston, which used to be the tech center for America in the sixties and seventies, before being supplanted by Silicon Valley as a tech hub; why did that revolution happen? It required four things: First, it needed an institution like Stanford, which was truly interested in primary research. Primary research, not commercial. Second, it needed the VC and the capital markets around it to fund those ideas and commercialize it by working with the universities. Third, you can’t do that unless you have a local talent pool that actually sits in that ecosystem and takes those ideas to market. Fourth, you need government support to allow people to take risks.” However, the executive cautioned, “If you don’t have purely research-oriented, high-end research institutions, a talent hub, and a capital market that actually funds it, then in the long term it’s not sustainable.” With this feedback in mind, it seems that the AI landscape in the UK is on solid footing. For example, the aforementioned developer training courses that Salesforce is now offering through its new London-based AI Center have the backing of City Hall, with the Deputy Mayor of London for Business and Growth, Howard Dawber, present at the opening ceremony. Meanwhile, the city has a wealth of internationally competitive research universities such as University College London, the London School of Economics and King’s College London, all of which further the progress of foundational data science research and collaborate with national programs such as the Alan Turing Institute, the UK’s national institute for data science and AI, founded in 2015 and largely funded by the UK government. The Alan Turing Institute is based in London’s ‘Knowledge Quarter’, a focal point around the King’s Cross area of London that houses other national institutes such as the Francis Crick Institute in addition to major tech firms like Google, who chose the site for its UK headquarters. This critical mass of educational institutions, national programs, high levels of investment and the presence of major tech enterprises all point towards a solid future for AI in London and the UK. Where next for AI centers? While it’s still too soon to see how much of an impact the AI centers discussed above will bring, their presence alone suggests a bright future. Whether it’s Salesforce in London or Microsoft in Sweden, such sizable investments to open such ambitious AI operations will be made after careful consideration. The fact that these locations have been chosen to host an AI center means they already boast the requisite parts of the AI ecosystem required. The British are famous for innovating around inefficiencies, which suggests that the new center will be able to navigate around any future hurdles that arise. Erick Espinosa on 150Sec London remains the number-one tech city outside of the US, based on Dealroom’s analysis that used a combination of factors including funding, patents, and billion-dollar-plus exits. number-one tech city The breadth of the activity can be noted during London Tech Week , an annual five-day event that brings together international enterprise leaders, government officials, and a dynamic expo to thousands of attendees. London Tech Week During the 2024 edition, held in the London Olympia in June, the hype around artificial intelligence (AI) in the UK’s capital could be heard loud and clear. artificial intelligence (AI) In a strategically timely move, Salesforce unveiled the opening of its first dedicated AI Center on the eve of London Tech Week, coinciding with the company’s own London World Tour. AI Center Salesforce’s new AI Center will see a 40,000-square-foot facility opened up in the Blue Fin Building, an iconic landmark based on the south bank of the River Thames in Southwark, and will host a range of AI training and up-skilling programs while also building important connections between tech companies, specialists and Salesforce partners. iconic landmark But why did Salesforce opt for London, and was this decision a smart move? A vote of confidence for London A vote of confidence for London Although Salesforce looks likely to open more AI Centers in key international locations in the future, its decision to opt for London as the first host city for this major endeavor speaks volumes for the potential of the city. Further, it looks like Salesforce isn’t the only tech firm that views London in a positive light with AI development. Research by London & Partners, one of the driving forces behind London Tech Week, found that some 77% of decision-makers at major international AI firms consider the city to be “more supportive of AI development than other major AI hubs,” according to Managing Director Janet Coyle. It’s also interesting to note that the AI Center isn’t the first play that Salesforce has made here. The company has committed $4 billion to invest in AI innovation over the next five years under Salesforce Ventures. $4 billion To date, $200 million has already been awarded to UK AI firms including Eleven Labs, a provider of an AI-powered text-to-speech and voice generator, and the procurement platform AutoGen AI to name a couple of examples. The presence of the Salesforce Ventures fund coupled with the AI center shows a clear vote of confidence for London’s future in AI, but its success is likely to hinge on the wider tech ecosystem in the capital. The rise of AI centers in Europe The rise of AI centers in Europe However, it seems that London will be one of many cities hosting dedicated AI centers soon. For instance, Microsoft plans to invest $3.2 billion in AI facilities in Sweden, its largest-ever infrastructure bet in the Nordic country. The enterprise Data4 also announced this month plans to develop an AI center outside of Athens, signaling a boost to Greece’s digital infrastructure and economy. To understand more about the benefits of AI centers we spoke to Ranjit Tinaikar, CEO of Ness Digital Engineering (Ness), which earlier this year launched an innovation center in Europe. Speaking on the growth of AI centers globally and the opening of Salesforce’s inaugural AI Center in London, Tinaikar explained, “I think AI Centers are a very valid idea. And I completely understand the goals of becoming the R&D hub for driving AI innovation.” At the same time, he stressed the importance of the ecosystem’s foundation for determining the success of such programs. “If you were to compare Boston, which used to be the tech center for America in the sixties and seventies, before being supplanted by Silicon Valley as a tech hub; why did that revolution happen? It required four things: First, it needed an institution like Stanford, which was truly interested in primary research. Primary research, not commercial. Second, it needed the VC and the capital markets around it to fund those ideas and commercialize it by working with the universities. Third, you can’t do that unless you have a local talent pool that actually sits in that ecosystem and takes those ideas to market. Fourth, you need government support to allow people to take risks.” However, the executive cautioned, “If you don’t have purely research-oriented, high-end research institutions, a talent hub, and a capital market that actually funds it, then in the long term it’s not sustainable.” With this feedback in mind, it seems that the AI landscape in the UK is on solid footing. For example, the aforementioned developer training courses that Salesforce is now offering through its new London-based AI Center have the backing of City Hall, with the Deputy Mayor of London for Business and Growth, Howard Dawber, present at the opening ceremony. Meanwhile, the city has a wealth of internationally competitive research universities such as University College London, the London School of Economics and King’s College London, all of which further the progress of foundational data science research and collaborate with national programs such as the Alan Turing Institute, the UK’s national institute for data science and AI, founded in 2015 and largely funded by the UK government. The Alan Turing Institute is based in London’s ‘ Knowledge Quarter ’, a focal point around the King’s Cross area of London that houses other national institutes such as the Francis Crick Institute in addition to major tech firms like Google, who chose the site for its UK headquarters. Knowledge Quarter This critical mass of educational institutions, national programs, high levels of investment and the presence of major tech enterprises all point towards a solid future for AI in London and the UK. Where next for AI centers? Where next for AI centers? While it’s still too soon to see how much of an impact the AI centers discussed above will bring, their presence alone suggests a bright future. Whether it’s Salesforce in London or Microsoft in Sweden, such sizable investments to open such ambitious AI operations will be made after careful consideration. The fact that these locations have been chosen to host an AI center means they already boast the requisite parts of the AI ecosystem required. The British are famous for innovating around inefficiencies, which suggests that the new center will be able to navigate around any future hurdles that arise. Erick Espinosa on 150Sec Erick Espinosa on 150Sec Erick Espinosa on 150Sec Erick Espinosa

Why Salesforce Chose London for Its First AI Center and $4 Billion AI Investment

Proč si Salesforce vybral Londýn pro své první centrum AI a investici do AI ve výši 4 miliard dolarů

Hvorfor Salesforce valgte London til sit første AI-center og $4 milliarder AI-investering

Por qué Salesforce eligió Londres para su primer centro de inteligencia artificial y una inversión de 4.000 millones de dólares en inteligencia artificial

Zergatik aukeratu zuen Salesforcek Londres bere lehen AI zentrorako eta 4.000 milioi dolar AI inbertsiorako

Por que Salesforce elixiu Londres para o seu primeiro centro de intelixencia artificial e un investimento en intelixencia artificial de 4.000 millóns de dólares

Perché Salesforce ha scelto Londra per il suo primo centro di intelligenza artificiale e un investimento di 4 miliardi di dollari in intelligenza artificiale

Salesforce が初の AI センターと 40 億ドルの AI 投資の拠点としてロンドンを選んだ理由

Kāpēc Salesforce izvēlējās Londonu savam pirmajam AI centram un 4 miljardu dolāru investīcijām AI

Waarom Salesforce Londen koos voor zijn eerste AI-centrum en $ 4 miljard aan AI-investeringen

เหตุใด Salesforce จึงเลือกลอนดอนเป็นศูนย์ AI แห่งแรกและลงทุน AI มูลค่า 4 พันล้านดอลลาร์

Kutheni i-Salesforce ikhethe iLondon kwiziko layo lokuqala le-AI kunye ne-4 yeebhiliyoni zeerandi zoTyalo-mali lwe-AI

Kungani i-Salesforce Ikhethe iLondon Isikhungo sayo sokuqala se-AI kanye ne- $ 4 Billion AI Investment

Hey there! It's exciting to see how AI changes how we work and create stuff. I've been trying out many new AI tools recently, and I want to share my favorite picks for June 2024. These tools are amazing and could help you whether you're making content, running a business, or just curious about AI. https://youtu.be/OdtaNo_ai4c?si=syOfu4Q4FxeLz2oS&embedable=true Visit my YouTube channel Simplified: All-in-One AI Helper for Design and Marketing First up is Simplified (https://simplified.com/). This tool is like a Swiss Army knife for creators and marketers. It's got everything you need in one place, which is super handy. What can you do with Simplified? Make designs: You can create thumbnails, ads, and presentations easily.
Edit videos: It's kind of like having a video editor right on your computer.
Write stuff: Need help writing a blog post? Simplified can help with that, too.
Manage social media: You can handle all your social media accounts from one spot.
And more: There are tools for managing projects and making chatbots. All of this costs about $24 a month. That's a pretty good deal for so many tools, and what I like is that everything is in one place. ElevenLabs: Make Computer Voices Sound Real Next on my list is ElevenLabs (https://elevenlabs.io/). This AI makes computer-generated voices sound super realistic. It's perfect for making audiobooks, adding voice to videos, or creating voice assistants for apps or websites. What's cool about ElevenLabs? It makes natural-sounding voices.
You can turn text into speech.
You can clone voices (like making a copy of your own voice). The voice cloning thing is really neat. Imagine being able to create content in your voice without having to record everything yourself! It costs $11 a month for 120 minutes of audio. Guidde: Make How-To Guides Super Easy Guidde (https://www.guidde.com/) is a tool that's going to make creating tutorials and guides so much easier. It's great for businesses making training materials or for anyone who makes how-to content. How does Guidde work? You set up an extension into your web browser.
You do the thing you want to make a guide for.
Guidde watches what you do and makes a step-by-step guide automatically.
You can even add a voice to explain things. You can start using Guidde for free, and there are paid plans if you need more features. WebSim: Play Around with Website Ideas Last but not least is WebSim (https://websim.ai/). This tool lets you create and test website ideas without knowing how to code. It's super fun to play with, especially if you're into web design or just like to experiment with website ideas. What can you do with WebSim? Create website mock-ups that actually work.
Try to copy existing websites (it's not perfect, but it's close).
Make simple games and apps. This tool is great for trying out new design ideas or seeing how a website might work. WebSim is still new, so we'll probably see it do even more cool stuff in the future. Wrapping It Up As we go through 2024, we'll probably see even more amazing AI tools pop up. It's a good idea to stay curious and try out new things. Don't be scared to use these AI tools in your work or projects. They might help you come up with your next big idea or make your work a lot easier. Just keep in mind that while these tools are super helpful, it's still up to us to use them in smart and creative ways. Technology is awesome, but it's how we use it that really matters. Don't forget to share your favorite AI tools in the comments below. Cheers! :) P.S. Don't forget to subscribe to my YouTube channel. :) Hey there! It's exciting to see how AI changes how we work and create stuff. I've been trying out many new AI tools recently, and I want to share my favorite picks for June 2024. These tools are amazing and could help you whether you're making content, running a business, or just curious about AI. https://youtu.be/OdtaNo_ai4c?si=syOfu4Q4FxeLz2oS&embedable=true https://youtu.be/OdtaNo_ai4c?si=syOfu4Q4FxeLz2oS&embedable=true Visit my YouTube channel Visit my YouTube channel Visit my YouTube channel Visit my YouTube channel Simplified: All-in-One AI Helper for Design and Marketing First up is Simplified (https://simplified.com/). This tool is like a Swiss Army knife for creators and marketers. It's got everything you need in one place, which is super handy. What can you do with Simplified? What can you do with Simplified? Make designs: You can create thumbnails, ads, and presentations easily. Edit videos: It's kind of like having a video editor right on your computer. Write stuff: Need help writing a blog post? Simplified can help with that, too. Manage social media: You can handle all your social media accounts from one spot. And more: There are tools for managing projects and making chatbots. Make designs: You can create thumbnails, ads, and presentations easily. Edit videos: It's kind of like having a video editor right on your computer. Write stuff: Need help writing a blog post? Simplified can help with that, too. Manage social media: You can handle all your social media accounts from one spot. And more: There are tools for managing projects and making chatbots. All of this costs about $24 a month . That's a pretty good deal for so many tools, and what I like is that everything is in one place. $24 a month ElevenLabs: Make Computer Voices Sound Real Next on my list is ElevenLabs (https://elevenlabs.io/). This AI makes computer-generated voices sound super realistic. It's perfect for making audiobooks, adding voice to videos, or creating voice assistants for apps or websites. What's cool about ElevenLabs? What's cool about ElevenLabs? It makes natural-sounding voices. You can turn text into speech. You can clone voices (like making a copy of your own voice). It makes natural-sounding voices. You can turn text into speech. You can clone voices (like making a copy of your own voice). The voice cloning thing is really neat. Imagine being able to create content in your voice without having to record everything yourself! It costs $11 a month for 120 minutes of audio. $11 a month for 120 minutes of audio. Guidde: Make How-To Guides Super Easy Guidde (https://www.guidde.com/) is a tool that's going to make creating tutorials and guides so much easier. It's great for businesses making training materials or for anyone who makes how-to content. How does Guidde work? How does Guidde work? You set up an extension into your web browser. You do the thing you want to make a guide for. Guidde watches what you do and makes a step-by-step guide automatically. You can even add a voice to explain things. You set up an extension into your web browser. You do the thing you want to make a guide for. Guidde watches what you do and makes a step-by-step guide automatically. You can even add a voice to explain things. You can start using Guidde for free, and there are paid plans if you need more features. WebSim: Play Around with Website Ideas Last but not least is WebSim (https://websim.ai/). This tool lets you create and test website ideas without knowing how to code. It's super fun to play with, especially if you're into web design or just like to experiment with website ideas. What can you do with WebSim? What can you do with WebSim? Create website mock-ups that actually work. Try to copy existing websites (it's not perfect, but it's close). Make simple games and apps. Create website mock-ups that actually work. Try to copy existing websites (it's not perfect, but it's close). Make simple games and apps. This tool is great for trying out new design ideas or seeing how a website might work. WebSim is still new, so we'll probably see it do even more cool stuff in the future. Wrapping It Up As we go through 2024, we'll probably see even more amazing AI tools pop up. It's a good idea to stay curious and try out new things. Don't be scared to use these AI tools in your work or projects. They might help you come up with your next big idea or make your work a lot easier. Just keep in mind that while these tools are super helpful, it's still up to us to use them in smart and creative ways. Technology is awesome, but it's how we use it that really matters. Don't forget to share your favorite AI tools in the comments below. Cheers! :) P.S. Don't forget to subscribe to my YouTube channel. :) subscribe to my YouTube

My Top 4 AI Picks for June 2024: Cool Tools You Should Check Out

Gartner predicts generative AI will become a general-purpose technology akin to the steam engine, electricity, and the internet. While the initial hype will fade, its impact will increase as innovative applications are discovered in daily work and life. Look at some of their predictions for AI, particularly Generative AI’s adoption: As entire industries, enterprises, and digital transformation specialists race to adopt this new tech, there are critical factors that must be taken into account. Improper adoption of technology can lead to disastrous results for the business and poor satisfaction for customers and employees who have to use them. I want to share what those factors are in this article. Why Integrate GenAI?
How to Implement GenAI?
What to Use GenAI For? Why Integrate GenAI? Value Proposition The primary reason to consider integrating GenAI is the substantial value it can bring to your business. GenAI has the potential to revolutionize various aspects of your operations, ultimately driving growth and efficiency. Top-Line Benefits One of the most significant advantages of GenAI is its ability to increase user engagement with your target segment, which can lead to higher revenue or new revenue streams altogether. By leveraging GenAI, you, as a business, can create more personalized and interactive content and enhance your customer experience and satisfaction with your products and services. For example, GenAI can help summarize and synthesize large amounts of information quickly, making it easier for users to find what they need and stay engaged with your platform. Bottom-Line Savings On the other hand, GenAI can also lead to significant cost reductions. Automating repetitive tasks such as customer support queries and data entry, allows your team to focus on more strategic activities. AI-powered tools can support your help center, providing instant responses to common questions and reducing the need for extensive human intervention. How to Implement GenAI? Building the GenAI Tech Stack Successful integration of GenAI requires a systematic approach. This involves Applications and use cases that align with your business goals


Choosing the right AI models to power these applications


Using the right infrastructure to deploy these applications and models


Handling the Implementation responsibly without harming trust and security while also upskilling employees and educating customers on how to use GenAI. Implementation and Application To start, you need to identify the areas within your business that will benefit most from AI integration. This could include content generation for marketing and sales, content conversion for streamlined communication, and productivity tools for various teams. Content Generation: Tools like Jasper, Writer.com, Byword, Adobe Firefly, and Midjourney can enhance your marketing efforts by generating high-quality content.


Content Conversion:  Platforms like Wondercraft (text to podcast), Vidyo (long-form video to short clips), Descript (podcasting), and ElevenLabs (AI voices) can transform how your marketing teams operate.


Spreadsheets and Slides: For financial and R&D teams, AI tools like Numerous.ai, Equals, Gamma, and Tome can automate data management and presentation tasks.


General Productivity: Tools like Vowel for meeting transcription and Metaview for interview recording can optimize daily operations. Ensuring Trust and Security Integrating AI also necessitates maintaining high standards of trust and security. Implement robust reporting mechanisms to allow users to flag AI-generated content that may be inaccurate or controversial. This helps build user trust and maintain content quality. Moreover, ethical training and fine-tuning of AI models are crucial. Ensure that user data is collected and used transparently and with explicit consent. Handle all user interactions responsibly to protect privacy and build confidence in your AI systems. What to Use GenAI For? Enhancing Employee Productivity Deploying AI applications can significantly boost employee productivity across various departments. As previously mentioned above, in the implementation and application layer of the GenAI tech stack, these are some use cases for employee productivity. Marketing and Sales: Tools like Jasper and Adobe Firefly can assist in generating compelling content for campaigns, while platforms like Byword enhance SEO efforts.
Content Conversion: AI tools such as Wondercraft and Vidyo can transform text and video content, making it more versatile and accessible.
Spreadsheets and Slides: AI-powered tools like Numerous.ai and Gamma streamline data processing and presentation creation, aiding teams in finance and R&D.
General Productivity: Vowel provides meeting transcription, while Metaview offers optimized recording and summarization for interviews. Poised serves as a personal communication coach, enhancing overall communication skills.
Customer Support: AI tools like Chatbase can automate customer service, providing immediate responses to queries and improving customer satisfaction.
Specific Applications for HR and Hiring Managers: For HR teams, GenAI tools like Metaview and Poised can revolutionize the recruitment process by recording and summarizing interviews, as well as coaching candidates on communication skills.
Customer Support: In customer support, tools like Chatbase can handle routine queries efficiently, freeing up human agents to tackle more complex issues. Integrating GenAI into your business can unlock new levels of efficiency, engagement, and cost savings. So consider the value it brings, the implementation process, and the specific applications for your business, and you can ensure a successful integration of GenAI that drives your business forward. Embrace the future of AI to stay competitive and innovative in today’s rapidly evolving landscape. Hope that helps! Gartner predicts generative AI will become a general-purpose technology akin to the steam engine, electricity, and the internet. While the initial hype will fade, its impact will increase as innovative applications are discovered in daily work and life. Look at some of their predictions for AI, particularly Generative AI’s adoption: As entire industries, enterprises, and digital transformation specialists race to adopt this new tech, there are critical factors that must be taken into account. Improper adoption of technology can lead to disastrous results for the business and poor satisfaction for customers and employees who have to use them. I want to share what those factors are in this article. Why Integrate GenAI? How to Implement GenAI? What to Use GenAI For? Why Integrate GenAI? How to Implement GenAI? What to Use GenAI For? Why Integrate GenAI? Value Proposition Value Proposition Value Proposition The primary reason to consider integrating GenAI is the substantial value it can bring to your business. GenAI has the potential to revolutionize various aspects of your operations, ultimately driving growth and efficiency. Top-Line Benefits Top-Line Benefits Top-Line Benefits One of the most significant advantages of GenAI is its ability to increase user engagement with your target segment, which can lead to higher revenue or new revenue streams altogether. By leveraging GenAI, you, as a business, can create more personalized and interactive content and enhance your customer experience and satisfaction with your products and services. For example, GenAI can help summarize and synthesize large amounts of information quickly, making it easier for users to find what they need and stay engaged with your platform. Bottom-Line Savings Bottom-Line Savings Bottom-Line Savings On the other hand, GenAI can also lead to significant cost reductions. Automating repetitive tasks such as customer support queries and data entry, allows your team to focus on more strategic activities. AI-powered tools can support your help center, providing instant responses to common questions and reducing the need for extensive human intervention. How to Implement GenAI? Building the GenAI Tech Stack Building the GenAI Tech Stack Building the GenAI Tech Stack Successful integration of GenAI requires a systematic approach. This involves Applications and use cases that align with your business goals Choosing the right AI models to power these applications Using the right infrastructure to deploy these applications and models Handling the Implementation responsibly without harming trust and security while also upskilling employees and educating customers on how to use GenAI. Applications and use cases that align with your business goals Applications and use cases that align with your business goals Choosing the right AI models to power these applications Choosing the right AI models to power these applications Using the right infrastructure to deploy these applications and models Using the right infrastructure to deploy these applications and models Handling the Implementation responsibly without harming trust and security while also upskilling employees and educating customers on how to use GenAI. Handling the Implementation responsibly without harming trust and security while also upskilling employees and educating customers on how to use GenAI. Implementation and Application Implementation and Application Implementation and Application To start, you need to identify the areas within your business that will benefit most from AI integration. This could include content generation for marketing and sales, content conversion for streamlined communication, and productivity tools for various teams. Content Generation: Tools like Jasper, Writer.com, Byword, Adobe Firefly, and Midjourney can enhance your marketing efforts by generating high-quality content. Content Conversion:  Platforms like Wondercraft (text to podcast), Vidyo (long-form video to short clips), Descript (podcasting), and ElevenLabs (AI voices) can transform how your marketing teams operate. Spreadsheets and Slides: For financial and R&D teams, AI tools like Numerous.ai, Equals, Gamma, and Tome can automate data management and presentation tasks. General Productivity: Tools like Vowel for meeting transcription and Metaview for interview recording can optimize daily operations. Content Generation: Tools like Jasper, Writer.com, Byword, Adobe Firefly, and Midjourney can enhance your marketing efforts by generating high-quality content. Content Generation: Tools like Jasper, Writer.com, Byword, Adobe Firefly, and Midjourney can enhance your marketing efforts by generating high-quality content. Content Generation: Content Conversion:  Platforms like Wondercraft (text to podcast), Vidyo (long-form video to short clips), Descript (podcasting), and ElevenLabs (AI voices) can transform how your marketing teams operate. Content Conversion :  Platforms like Wondercraft (text to podcast), Vidyo (long-form video to short clips), Descript (podcasting), and ElevenLabs (AI voices) can transform how your marketing teams operate. Content Conversion Spreadsheets and Slides: For financial and R&D teams, AI tools like Numerous.ai, Equals, Gamma, and Tome can automate data management and presentation tasks. Spreadsheets and Slides : For financial and R&D teams, AI tools like Numerous.ai, Equals, Gamma, and Tome can automate data management and presentation tasks. Spreadsheets and Slides General Productivity: Tools like Vowel for meeting transcription and Metaview for interview recording can optimize daily operations. General Productivity : Tools like Vowel for meeting transcription and Metaview for interview recording can optimize daily operations. General Productivity Ensuring Trust and Security Ensuring Trust and Security Ensuring Trust and Security Integrating AI also necessitates maintaining high standards of trust and security. Implement robust reporting mechanisms to allow users to flag AI-generated content that may be inaccurate or controversial. This helps build user trust and maintain content quality. Moreover, ethical training and fine-tuning of AI models are crucial. Ensure that user data is collected and used transparently and with explicit consent. Handle all user interactions responsibly to protect privacy and build confidence in your AI systems. What to Use GenAI For? Enhancing Employee Productivity Enhancing Employee Productivity Enhancing Employee Productivity Deploying AI applications can significantly boost employee productivity across various departments. As previously mentioned above, in the implementation and application layer of the GenAI tech stack, these are some use cases for employee productivity. Marketing and Sales: Tools like Jasper and Adobe Firefly can assist in generating compelling content for campaigns, while platforms like Byword enhance SEO efforts. Content Conversion: AI tools such as Wondercraft and Vidyo can transform text and video content, making it more versatile and accessible. Spreadsheets and Slides: AI-powered tools like Numerous.ai and Gamma streamline data processing and presentation creation, aiding teams in finance and R&D. General Productivity: Vowel provides meeting transcription, while Metaview offers optimized recording and summarization for interviews. Poised serves as a personal communication coach, enhancing overall communication skills. Customer Support: AI tools like Chatbase can automate customer service, providing immediate responses to queries and improving customer satisfaction. Specific Applications for HR and Hiring Managers: For HR teams, GenAI tools like Metaview and Poised can revolutionize the recruitment process by recording and summarizing interviews, as well as coaching candidates on communication skills. Customer Support: In customer support, tools like Chatbase can handle routine queries efficiently, freeing up human agents to tackle more complex issues. Marketing and Sales: Tools like Jasper and Adobe Firefly can assist in generating compelling content for campaigns, while platforms like Byword enhance SEO efforts. Marketing and Sales: Content Conversion: AI tools such as Wondercraft and Vidyo can transform text and video content, making it more versatile and accessible. Content Conversion: Spreadsheets and Slides: AI-powered tools like Numerous.ai and Gamma streamline data processing and presentation creation, aiding teams in finance and R&D. Spreadsheets and Slides: General Productivity: Vowel provides meeting transcription, while Metaview offers optimized recording and summarization for interviews. Poised serves as a personal communication coach, enhancing overall communication skills. General Productivity: Customer Support: AI tools like Chatbase can automate customer service, providing immediate responses to queries and improving customer satisfaction. Customer Support: Specific Applications for HR and Hiring Managers: For HR teams, GenAI tools like Metaview and Poised can revolutionize the recruitment process by recording and summarizing interviews, as well as coaching candidates on communication skills. Specific Applications for HR and Hiring Managers: Customer Support: In customer support, tools like Chatbase can handle routine queries efficiently, freeing up human agents to tackle more complex issues. Customer Support: Integrating GenAI into your business can unlock new levels of efficiency, engagement, and cost savings. So consider the value it brings, the implementation process, and the specific applications for your business, and you can ensure a successful integration of GenAI that drives your business forward. Embrace the future of AI to stay competitive and innovative in today’s rapidly evolving landscape. Hope that helps!

3 Things to Consider Before Adding GenAI to Your Business

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