Tag: WhatsApp Business API

How to Use WhatsApp Broadcasts and AI for Better ROI.

In the digital marketing landscape, WhatsApp Broadcasts have emerged as a modern-day equivalent of flyers, combining efficiency with precision targeting. The integration of Artificial Intelligence (AI) further amplifies its potential, offering smarter ways to connect with and engage audiences. With a staggering 98% open rates and 35% click rates, leveraging WhatsApp Broadcasts with AI can significantly boost your Return on Investment (ROI). This guide delves into strategies for building a robust broadcast list and utilizing AI to maximize the impact of your WhatsApp marketing campaign.

Building a WhatsApp Broadcast List with AI

In the world of digital marketing, WhatsApp Broadcasts are like the modern-day equivalent of flyers. They offer a combination of efficiency and precision targeting that can help businesses reach their audiences in a whole new way. But what if I told you that you could take your WhatsApp Broadcasts to the next level with the power of Artificial Intelligence (AI)? By leveraging AI, you can unlock even more potential and significantly boost your Return on Investment (ROI).

WhatsApp Broadcasts already boast impressive statistics, with a staggering 98% open rate and 35% click rate. But imagine what you could achieve by integrating AI into your WhatsApp marketing campaigns.

Let’s start by exploring how AI can help you build a WhatsApp Broadcast list. WhatsApp offers several built-in features that can be enhanced with AI. For example, with the WhatsApp Business API, AI can analyze customer interactions and create personalized opt-in invitations. This way, you can leverage AI to attract more subscribers to your broadcast list.

Another feature you can use is the WhatsApp Click-to-Chat Link. By using AI algorithms to analyze user engagement data, you can determine the most effective platforms to place these links. This will help drive more users to engage with your WhatsApp Broadcasts.

QR codes have become increasingly popular in marketing, and WhatsApp offers its own QR code feature. By using AI algorithms to track QR code scans and optimize their placements, you can make sure that your QR codes are working to their full potential.

If you have a website, you can also utilize the WhatsApp Chat Widget. AI can personalize the interactions on the chat widget, improving user engagement and encouraging visitors to join your broadcast list.

Let’s move on to how you can utilize AI in the content and engagement strategies of your WhatsApp marketing campaigns.

AI can help you create personalized newsletters by analyzing subscriber preferences. By tailoring your newsletter content to match what your subscribers are interested in, you can encourage them to provide their WhatsApp details and join your broadcast list.

When it comes to content strategy, AI can be a powerful tool. You can use AI tools to analyze trending topics and user interests for your blogs and glossaries, ensuring that your content remains relevant and engaging. Additionally, AI can help you segment your audience and offer personalized eBooks, reports, and whitepapers to different user groups.

Product demos and samples are a great way to engage potential leads, but AI can take it a step further. By deploying AI to identify leads that are most likely to respond positively to product demos and samples, you can focus your efforts on those who are most likely to convert.

Workshops and webinars are another effective way to engage with your audience. With AI tools, you can identify trending topics and personalize invitations, increasing registration rates and ensuring that you are reaching the right people.

Social media is a valuable platform for marketing, and AI can help you make the most of it. AI algorithms can analyze social media behavior to identify potential leads and optimize your content, ensuring that you are reaching the right audience at the right time.

When it comes to social media ads, AI can help you fine-tune your targeting. By leveraging AI to analyze user behavior and preferences, you can ensure that your ads are being shown to the people who are most likely to be interested in your products or services.

Chatbots have become increasingly popular in customer service, and for a good reason. By integrating AI-powered chatbots into your social media platforms, you can handle complex queries and provide personalized interactions. This can greatly improve customer satisfaction and engagement.

Customer referral programs are a valuable tool for growing your business, and AI can help you make them even more effective. By using AI analytics, you can identify customers who are most likely to refer others and tailor your referral programs accordingly.

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Now let’s focus on how you can maximize your ROI with WhatsApp Broadcasts and AI.

First and foremost, AI-driven personalization is key. By using AI to segment your audience, you can send highly personalized and relevant broadcasts. This will ensure that your messages resonate with your audience, increasing engagement and conversion rates.

Timing is everything, and AI can help you with that too. By leveraging AI, you can determine the best times to send follow-up messages and analyze customer responses for future interactions. This will help you build a strong relationship with your audience.

Continuous AI analytics are crucial for optimizing your WhatsApp Broadcasts. By employing AI tools to analyze the performance of your broadcasts, you can adapt your strategies accordingly. This will help you stay ahead of the game and ensure that you are delivering the most effective messages to your audience.

It’s important to remember that while AI is a powerful tool, it should be used in adherence to best practices and compliance policies. This will ensure that your communication is respectful and effective, building a positive reputation for your business.

Finally, integrating WhatsApp and AI into a broader digital marketing strategy is essential. While WhatsApp Broadcasts and AI are powerful on their own, incorporating them into a comprehensive strategy will result in synergistic effects. This means that you should integrate WhatsApp and AI with other marketing channels and tactics to create a unified and effective approach.

In conclusion, combining WhatsApp Broadcasts with AI offers a powerful opportunity to enhance your digital marketing efforts. By strategically building a broadcast list and employing AI for personalized, data-driven communication, businesses can achieve a significantly improved ROI.

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But where can you get your hands on this gem? Look no further than popular online platforms like Etsy, Shopify, Apple, Google, or Amazon. They’ve got you covered and ready to embark on your AI adventure.

1. Leveraging WhatsApp’s Built-In Features

• WhatsApp Business API: Use AI to analyze customer interactions and create personalized opt-in invitations.
• WhatsApp Click-to-Chat Link: AI can determine the most effective platforms to place these links based on user engagement data.
• WhatsApp QR Code: Use AI algorithms to track QR code scans and optimize their placements.
• WhatsApp Chat Widget: AI can personalize chat widget interactions on your website, improving user engagement.

2. AI-Powered Newsletters

• Utilize AI to analyze subscriber preferences and tailor newsletter content, encouraging users to provide their WhatsApp details.

3. AI-Enhanced Content Strategy

• Free Content: Use AI tools to analyze trending topics and user interests for your blogs and glossaries.
• Gated Content: AI can help segment audiences and offer them personalized eBooks, reports, and whitepapers.

4. Product Demos and Samples with AI

• Deploy AI to identify potential leads who are most likely to respond positively to product demos and samples.

5. AI-Driven Workshops and Webinars

• AI tools can help identify trending topics and personalize invitations to increase registration rates.

6. Social Media Insights with AI

• AI algorithms can analyze social media behavior to identify potential leads and optimize content.

7. Targeted AI-Enabled Social Media Ads

• Leverage AI to fine-tune your ad targeting based on user behavior and preferences.

8. Chatbots and AI Conversations

• Integrate AI-powered chatbots to handle complex queries and provide personalized interactions on social media.

9. Customer Referral Programs with AI Analytics

• Use AI to identify customers most likely to refer others and tailor referral programs accordingly.

Maximizing ROI with WhatsApp Broadcasts and AI

After building your list, the next step is to harness the power of WhatsApp Broadcasts and AI for maximum ROI.

1. AI-Driven Personalization: Use AI to segment your audience and send highly personalized and relevant broadcasts.
2. Timely AI-Enhanced Follow-Ups: Leverage AI to determine the best times for follow-up messages and to analyze customer responses for future interactions.
3. Continuous AI Analytics: Employ AI tools to continuously analyze the performance of your broadcasts and adapt strategies accordingly.
4. Adherence to Best Practices: Combine AI insights with WhatsApp’s compliance policies to ensure respectful and effective communication.
5. Integrating WhatsApp and AI into a Broader Strategy: Don’t rely solely on WhatsApp and AI. Integrate them into a comprehensive digital marketing strategy for synergistic effects.

If you are not comfortable with AI, you can still leverage WhatsApp broadcast for a good ROI.

1. WhatsApp’s Built-In Features

• WhatsApp Business API: Utilizes an opt-in policy encouraging new users to connect with your business.
• WhatsApp Click-to-Chat Link: This feature allows you to create a clickable link for your WhatsApp business number, making it easier for customers to reach out directly.
• WhatsApp QR Code: Similar to Click-to-Chat but in a scannable QR format. Ideal for offline and online platforms.
• WhatsApp Chat Widget: Integrates a chat feature on your website, directly linking to your WhatsApp business account.

2. Create a Newsletter

• Offer subscriptions for updates about your business and industry, encouraging users to register with their email and WhatsApp details.

3. Content Strategy

• Free Content: Blogs and glossaries to increase awareness and credibility.
• Gated Content: eBooks, reports, and whitepapers for detailed insights, in exchange for contact details.

4. Product Demos and Samples

• Entice potential leads with a ‘free taste’ of your product or service in exchange for contact information.

5. Engaging Workshops and Webinars

• Host informative sessions in exchange for registration, thus acquiring leads.

6. Social Media Utilization

• Leverage the extensive reach of platforms like Facebook and Instagram to gather leads.

7. Paid Social Media Ads

• Target specific demographics with sponsored ads to attract a relevant audience.

8. Chatbot Integration

• Use automated chatbots to engage users on social media, covering FAQs and product details.

9. Customer Referral Programs

• Encourage current customers to refer friends in exchange for exclusive offers.

Maximizing Returns with WhatsApp Broadcasts

Once you’ve built a robust list, it’s crucial to maximize the potential of WhatsApp Broadcasts. Here’s how:

1. Targeted Content: Ensure that your broadcasts are relevant and engaging. Personalize messages based on user behavior and preferences.
2. Timely Follow-Ups: Use the high open rates to your advantage. Send follow-up messages to keep the conversation going.
3. Measure and Adapt: Track the success of your broadcasts. Use insights to refine your strategy continually.
4. Compliance and Consent: Always adhere to WhatsApp’s policies and respect user consent for message receipts.
5. Integrated Marketing Strategy: Don’t rely solely on WhatsApp. Integrate it into a broader digital marketing strategy for maximum impact.

Conclusion

Combining WhatsApp Broadcasts with AI presents a powerful opportunity to enhance your digital marketing efforts. By smartly building a broadcast list and employing AI for personalized, data-driven communication, businesses can achieve a significantly improved ROI. Remember, the key lies in the strategic, innovative, and ethical use of these technologies to create meaningful connections with your audience.

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Latest Marketing Trends in December 2023

A Daily Chronicle of AI Innovations in December 2023

• My first attempt at truly creating something with ChatGPT - AI Shopping Assistance
  by /u/Talhelfg (Artificial Intelligence Gateway) on December 7, 2024 at 4:07 pm

  Hey everyone! So, my idea was quite simple - 1. Get the Amazon product URL. 2. Query Amazon for similar products. 3. Let the AI choose the best offer or alternative. At first, it seemed like the simplest idea ever. However, AI still struggles with basic concepts that I find straightforward. For instance, if I search for “iPhone,” it will find a case and happily say, “I just saved you 99%!”. I’m trying to avoid using taxonomy, but I couldn’t get good results without explicitly telling the AI to ignore items like cases, screen protectors, and so on. Unfortunately, it couldn’t comprehend this on its own. I believe I’ve figured out most of the issues, but I’m still working on it. Please let me know if you find this useful. submitted by /u/Talhelfg [link] [comments]

• The history of ML reveals why LLM progress is slowing
  by /u/contextbot (Artificial Intelligence Gateway) on December 7, 2024 at 3:56 pm

  “Thanks to decades of data creation and graphics innovation, we advanced incredibly quickly for a few years. But we’ve used up these accelerants and there’s none left to fuel another big leap. Our gains going forward will be slow, incremental, and hard-fought. As Gary Marcus wrote last week, “scaling laws aren’t really laws anymore.” “Reviewing the history of machine learning, we can both understand how the field advanced so quickly and why LLMs have hit a wall.” Original Link: https://www.dbreunig.com/2024/12/05/why-llms-are-hitting-a-wall.html submitted by /u/contextbot [link] [comments]

• How to solve token exhausted problem in Vision Language Models
  by /u/Difficult-Race-1188 (Artificial Intelligence Gateway) on December 7, 2024 at 2:44 pm

  Representing a single image in current LVLMs can require hundreds or even thousands of tokens. This results in significant computational costs, which grow quadratically as input image resolution increases, thereby severely impacting the efficiency of both training and inference. To address this challenge, researchers conducted an empirical study revealing that all visual tokens are necessary for LVLMs in the shallow layers, and token redundancy progressively increases in the deeper layers of the model. To this end, they propose PyramidDrop, a visual redundancy reduction strategy for LVLMs to boost their efficiency in both training and inference with neglectable performance loss. Original Article: https://medium.com/aiguys/are-tiny-transformers-the-future-of-scaling-e6802621ec57 The below image explains pretty well about the redundancy. We can clearly see that by the time we reach the 16th layer, we see very few activations. https://preview.redd.it/gglh6kxij65e1.png?width=828&format=png&auto=webp&s=48d88664493f297d277e13cdab5a07522b1a33f6 Imagine this scenario: https://preview.redd.it/ukjnnn4kj65e1.png?width=356&format=png&auto=webp&s=f5b1be98a470f71214a8617ca31efd8c1adac8f1 You have a small fleet of birds flying in the sky. When we pass this image to our vision models. Most of the tokens will look like this: [Sky, Sky, Sky, Skye,……Bird, Sky…Sky… Sky] In short, the [Sky] token will be repeated so many times. I should have conveyed the [Sky] token once and that should have been enough, but that’s not the case with most current Vision Language Models. And to solve this problem researchers introduce PyramidDrop. https://preview.redd.it/d05bce3lj65e1.png?width=622&format=png&auto=webp&s=e47bcbfb77a5b494c7e1280767bb9a0e7abd2971 Information for answering the instructions. With the layer increases, the redundancy of image tokens increases rapidly. At layer 16, even preserving only 10% of image tokens will not cause an obvious performance decline. Notably, at layer 24, the model performance is nearly irrelevant to the image tokens, indicating that the model has already captured the necessary image information and the image tokens are redundant for the model now. Previous research on image token compression typically drops image tokens before passing them to the language model or uses a fixed compression ratio across all language model layers. However, redundancy is not consistent across different layers. Redundancy of image tokens is relatively minimal in the shallow layers and becomes progressively larger in deeper layers. Thus, uniformly compressing image tokens across layers may lead to the loss of valuable information in the shallow layers while retaining unnecessary redundancy in the deeper layers. LVLM (Large Vision Langauge Models) pays attention to most of the image tokens at shallow layers and the attention to different tokens shows a uniform pattern. On the contrary, in the middle of the LVLMs, the attention shows a sparse pattern and mainly focuses on the question-related image's local parts. PyramidDrop, which fully leverages layer-wise redundancy to compress image tokens. To maximize training efficiency while preserving the essential information of the image tokens, PyramidDrop divides the forward pass of the LLM into multiple stages. In the shallow layers, we retain a higher proportion of image tokens to preserve the entire vision information. At the end of each stage, it partially drops the image tokens, until nearly all the image tokens are eliminated in the deeper layers. This approach allows us to optimize training efficiency while maintaining critical information. https://preview.redd.it/ty1eylomj65e1.png?width=828&format=png&auto=webp&s=572932ba20b204a32deb7e7448cf616fff22f07e Not only does this technique make the Infernce faster for LVLMs, but in some cases, it even increases the performance. But then the question is how can a smaller model with the same architecture perform better? We know from other experiments that giving too much context to LLMs, actually leads to a decrease in the performance. This seems to confuse the model about what is actually important in a given token sequence. But I have my own hypothesis on this, based on the research I read on Mechanistic interpretability. The idea here is that if the model has too many parameters, it will go more toward memorization, but if I reduce the number of parameters, the model is forced to learn the abstractions instead of relying on memorization. As we see in the Grokking. The model starts with Memorization, and by the time it reaches the generalization, almost all the parameters go close to zero, except the ones that strengthen the generalized solution of that problem. submitted by /u/Difficult-Race-1188 [link] [comments]

• Leveraging decentralized technologies and AI
  by /u/CuriousActive2322 (Artificial Intelligence Gateway) on December 7, 2024 at 2:15 pm

  Leveraging decentralized technologies and AI can revolutionize automation across various industries. Business Applications of AI Agent Networks It can offer significant opportunities for businesses. For example, a company could develop a network of specialized AI agents tailored to specific departments. These agents might analyze market trends, optimize marketing strategies, identify sales leads, and deliver customer support—all with minimal human intervention. Such automation could fundamentally transform operations, allowing AI agents to handle tasks typically requiring human oversight. This shift has the potential to increase efficiency, reduce costs, and free employees to focus on strategic initiatives. Towards Fully Autonomous Swarms The ultimate goal is to enable fully autonomous multi-agent systems, or "swarms." These systems possess the following key characteristics: Self-Directing: Once initiated, the swarm autonomously pursues its mission without supervision. It can adapt its actions based on heuristic principles or specific mission parameters. Self-Correcting: The swarm detects and addresses errors—whether technical, strategic, or epistemic—without external input. Self-Improving: Over time, the swarm enhances its capabilities, learning from its environment and experiences. Multi-Agent Systems and Decentralization Multi-agent systems (MAS) are composed of interacting intelligent agents that solve problems beyond the capacity of individual agents or monolithic systems. Recent advancements, such as large language models (LLMs), have enabled sophisticated interactions among these agents, opening new research avenues. Integrating MAS with blockchain introduces decentralized AI systems, which offer unprecedented benefits: Data security: Blockchain ensures data integrity through tamper-proof storage. Trust and transparency: Immutable records on blockchain foster confidence in AI decisions. Distributed intelligence: Decentralized networks enable collaboration among autonomous agents, enhancing efficiency. Challenges in Centralized AI and the Need for Decentralization Centralized AI systems face several issues, such as vulnerability to data tampering, lack of data provenance, and potential bias in decision-making. Blockchain technology addresses these concerns by enabling decentralized, trusted, and secure data storage and transactions. Smart contracts further allow programmable governance for data sharing and decision-making among agents. Advantages of Decentralized AI Systems: Enhanced Data Security: Blockchain's cryptographic architecture ensures sensitive data remains secure. Improved Trust: Transparent decision-making processes recorded on the blockchain increase public confidence in AI. Efficient Collaboration: Decentralized systems eliminate reliance on central authorities, fostering collective decision-making. Optimized Resource Use: Blockchain-based decentralized systems ensure scalable, efficient storage and data management. Synergies Between Blockchain and AI: The convergence of blockchain and AI unlocks transformative potential across industries. Key benefits include: Transparency: Blockchain's immutable ledger provides an auditable trail of AI decisions, addressing concerns about the "black box" nature of AI systems. Data Security: AI leverages blockchain’s decentralized architecture to enhance security and detect threats. Scalability: AI optimizes blockchain performance by improving consensus mechanisms and transaction validation. Data Monetization: Decentralized marketplaces powered by blockchain enable secure data sharing, with individuals maintaining control over their data. Applications Across Industries Healthcare: Systems can use blockchain for decentralized medical records, while AI processes this data for predictive analytics and personalized care. Supply Chain: Projects can integrate blockchain for traceability and AI for demand forecasting and fraud detection. Finance: Platforms can crowdsource AI models using blockchain, democratizing investment decision-making. Education: AI-powered learning systems leverage blockchain for secure data management and personalized education plans. IoT Security: Blockchain-secured IoT devices, combined with AI for threat detection, ensure robust security and uptime. Energy Management: Blockchain-enabled peer-to-peer energy trading, optimized by AI algorithms, promotes efficiency and cost savings. Opportunities and Challenges Neural Networks and Blockchain: By ensuring data integrity and fostering decentralized collaboration, blockchain enhances neural network applications in sectors like healthcare. However, the computational complexity of blockchain remains a challenge for real-time operations. Machine Learning: Blockchain promotes secure environments for decentralized model training. Yet, scalability and privacy concerns must be addressed. Natural Language Processing (NLP): Blockchain can validate information sources for NLP applications like chatbots. However, challenges include synchronizing dynamic language models with blockchain's immutable structure. Integrating AI with blockchain has the potential to reshape industries, offering systems that are more transparent, secure, and efficient. While technical and regulatory challenges remain, ongoing advancements in both fields promise streamlined solutions that fully realize the transformative power of decentralized AI. submitted by /u/CuriousActive2322 [link] [comments]

• Italian Startup iGenius Partners with Nvidia to Develop Major AI System
  by /u/SmythOSInfo (Artificial Intelligence Gateway) on December 7, 2024 at 12:51 pm

  On Thursday, Italian startup iGenius and Nvidia (NASDAQ: NVDA) announced plans to deploy one of the world’s largest installations of Nvidia’s latest servers by mid-next year in a data center located in southern Italy. The data center will house around 80 of Nvidia’s cutting-edge GB200 NVL72 servers, each equipped with 72 “Blackwell” chips, the company’s most powerful technology. iGenius, valued at over $1 billion, has raised €650 million this year and is securing additional funding for the AI computing system, named “Colosseum.” While the startup did not disclose the project's cost, CEO Uljan Sharka revealed the system is intended to advance iGenius’ open-source AI models tailored for industries like banking and healthcare, which prioritize strict data security. For Colosseum, iGenius is utilizing Nvidia’s suite of software tools, including Nvidia NIM, an app-store-like platform for AI models. These models, some potentially reaching 1 trillion parameters in complexity, can be seamlessly deployed across businesses using Nvidia chips. “With a click of a button, they can now pull it from the Nvidia catalog and implement it into their application,” Sharka explained. Colosseum will rank among the largest deployments of Nvidia’s flagship servers globally. Charlie Boyle, vice president and general manager of DGX systems at Nvidia, emphasized the uniqueness of the project, highlighting the collaboration between multiple Nvidia hardware and software teams with iGenius. “They’re really building something unique here,” Boyle told Reuters. Source: Abbo News submitted by /u/SmythOSInfo [link] [comments]

• If AI becomes highly advanced, could it develop its consciousness and desires?
  by /u/Imagine-your-success (Artificial Intelligence Gateway) on December 7, 2024 at 12:22 pm

  submitted by /u/Imagine-your-success [link] [comments]

• Shorts automaton.
  by /u/Jaxondevs (Artificial Intelligence Gateway) on December 7, 2024 at 9:05 am

  Hello r/ArtificalIntelligence , I was wondering if any of you amazing people will know a tool like the one below that doesn't use open ai, ChatGPT because I do not have a API funding, I would like something I could host or the ai API be free, if it is a easy code edit I would be willing to do it but thank your for the help and sorry if I sound dumb. https://github.com/RayVentura/ShortGPT submitted by /u/Jaxondevs [link] [comments]

• Has anyone used "Do Browser"? Looks promising
  by /u/sarrcom (Artificial Intelligence Gateway) on December 7, 2024 at 8:51 am

  Video here. Their website here. It's still under development, but apparently it can reply to emails, order pizza, and more. I'm not related to them in any way. submitted by /u/sarrcom [link] [comments]

• One-Minute Daily AI News 12/6/2024
  by /u/Excellent-Target-847 (Artificial Intelligence Gateway) on December 7, 2024 at 5:36 am

  OpenAI Is Working With Anduril to Supply the US Military With AI.[1] Meta unveils a new, more efficient Llama model.[2] Murdered Insurance CEO Had Deployed an AI to Automatically Deny Benefits for Sick People.[3] NYPD Ridiculed for Saying AI Will Find CEO Killer as They Fail to Name Suspect.[4] Sources included at: https://bushaicave.com/2024/12/06/12-6-2024/ submitted by /u/Excellent-Target-847 [link] [comments]

• Careers to get into after the rise of AI
  by /u/misobean56 (Artificial Intelligence Gateway) on December 7, 2024 at 3:21 am

  So, AI is progressing faster at a rate that has never been seen before. Im going to enter the workforce soon, as I am 16, and so I was wondering what careers to get into and not to get into because I know AI will probably change everything soon. My main interests are Computer Science and Political Science, but I don’t know how much AI is going to change those type of jobs. submitted by /u/misobean56 [link] [comments]

• Llama3.3 free API
  by /u/mehul_gupta1997 (Artificial Intelligence Gateway) on December 7, 2024 at 3:08 am

  Meta released Llama3.3 yesterday which is a 70B model outperforming Llama3.1 405B on various metrics. For usage, groq is providing a free API key for Llama3.3. Check out how to use it : https://youtu.be/ZQoPOuSbmZs?si=7gBuE-qCGa19Jbw1 submitted by /u/mehul_gupta1997 [link] [comments]

• Techno alchemy. When Ai meets ancient alchemical texts
  by /u/ShelterCorrect (Artificial Intelligence Gateway) on December 7, 2024 at 2:27 am

  Zosimos of Panopolis is the author of some of if not THE oldest texts recorded on alchemy. What if we were to merge the practise of ancient alchemy with modern day artificial intelligence? We’ll look no further because in Techno alchemy we do just that! https://youtu.be/jGF4HWELfRw?si=7pNCrqgibt_-9YX4 submitted by /u/ShelterCorrect [link] [comments]

• archgw - intelligent gateway for agents, offers fast function calling, intent routing, and rich observability as an open source project
  by /u/AdditionalWeb107 (Artificial Intelligence Gateway) on December 7, 2024 at 1:49 am

  I help with devrel for https://github.com/katanemo/archgw - an intelligent gateway for agents. Offers smart intent routing, fast function-calling, prompt guardrails and observability so that you can focus on the stuff that matters the most submitted by /u/AdditionalWeb107 [link] [comments]

• How do I get onto the startup/AI scene?
  by /u/toyheartz (Artificial Intelligence Gateway) on December 6, 2024 at 11:19 pm

  I was laid off and decided to use this time wisely to switch careers. Willing to do the hard work and I know it won’t be overnight but need a starting point to enter to ecosystem. ETA: i.e. like conferences to attend? Where does everyone building community? submitted by /u/toyheartz [link] [comments]

• AI and Relational Intelligence: Thoughts on a New Kind of Tech Understanding
  by /u/That-Pension4540 (Artificial Intelligence Gateway) on December 6, 2024 at 10:18 pm

  I've been doing some thinking and deep diving into AI research and came across a fascinating concept called 'relational intelligence.' Here's what it's all about: Most people think of AI as just number-crunching machines, but I'm fascinated by the potential for a more nuanced form of intelligence. Relational intelligence is more than AI mimicking human consciousness - it's about crafting systems that adapt to and genuinely understand context. Imagine AI that doesn't just respond mechanically, but actually synthesizes information dynamically. Think of it as the difference between a simple calculator and a conversation partner who actually gets the nuances of what you're saying. In fields like healthcare, education, and customer service, this could be revolutionary - systems that genuinely understand the complexity of human needs. We're not trying to create human-like consciousness, but something entirely new: an intelligence that complements human thinking while being uniquely its own thing. I'm really curious to hear your thoughts: 1. How do you see relational intelligence potentially transforming different industries? 2. Can AI develop a meaningful form of intelligence without human emotions? 3. What challenges might we face in developing this approach? Disclaimer: Just exploring ideas here, not claiming we've solved AI consciousness or anything. submitted by /u/That-Pension4540 [link] [comments]

• I really like this sub. I feel really at home here
  by /u/Libertyforzombies (Artificial Intelligence Gateway) on December 6, 2024 at 9:06 pm

  It's nice to find people who share my views on A.I. This doesn't mean (as some might try and tell me) that I covet an 'echo chamber' - it means I find it comforting to know that there are others who have the ability to use A.I. and A.I. language models (in my case) as a tool to help them achieve their goals. submitted by /u/Libertyforzombies [link] [comments]

• How do companies apply AI?
  by /u/trainermade (Artificial Intelligence Gateway) on December 6, 2024 at 8:54 pm

  I have used gpt’s for personal use, code samples, image generation and understand the general use cases. However, could someone help me understand how companies use AI. Meaning, I see several roles that require AI (tech and non-tech) and I want to understand deeper if they are requiring the use of gpt’s or something else. How does a company sandbox the AI’s information to just knowledge that the company doesn’t mind sharing. Taking as an example a supply chain co, or healthcare co, what would be the use case for AI? Are we building LLM’s from the ground up? Edit: for a little more specificity, I see the following in a lot of jd’s and would like to generically understand what this means ‘Knowledge of AI, machine learning, natural language processing, and computer vision technologies and applications’ submitted by /u/trainermade [link] [comments]

• I want to learn AI, how should i go about it?
  by /u/yoho7202 (Artificial Intelligence Gateway) on December 6, 2024 at 8:27 pm

  And by learn, I mean deep understanding of the concepts and be able to make stuff with AI. I got no idea where to start, what concepts should I learn and what concepts should I reserve for later. I'm already familiar with some data related stuff, I've done some data analysis and some machine learning but I feel like my understanding is just surface lvl. How do you recommend one should go about learning "AI", and what are some books/resources that you recommend? submitted by /u/yoho7202 [link] [comments]

• rapid prototyping for computer science and info tech educators
  by /u/coffeechug (Artificial Intelligence Gateway) on December 6, 2024 at 7:43 pm

  I'm designing an interactive AI coding workshop and am currently building out the section focused on rapid prototyping for computer science and info tech educators and leaders. I'm seeking insights and perspectives on free, accessible tools that enable beginner-friendly AI-assisted coding exploration for them to undergo a hands on exploration of learning so they can consider teaching and learning implications. Specific context for feedback: Target audience: Emerging developers/students Goal: Learn AI-assisted web application development Constraints: Zero-cost tools Duration: 30-minute interactive challenge Key questions for community input: Beyond Replit(agent has a cost) and bolt.new, what free platforms support AI-assisted coding for beginners/teachers/students? What web application concepts would effectively demonstrate coding/CS principles? What AI code generation tools provide the most educational value for novices? I'm particularly interested in platforms that: Offer free tiers Have integrated AI coding assistance Support web/frontend development Provide learning-oriented environments Would love insights from developers, educators, and tech community members on crafting an engaging, accessible learning experience! The goal is to design a session that demystifies AI-assisted development while keeping barriers to entry low. Here is my sample prompt I am testing out on several tools to give you a sense of what I am thinking: A web-based interactive learning platform designed to teach 5th-grade students about fundamental programming concepts (sequences, events, loops, and conditionals) aligned with CSTA standard 1B-AP-10. Core Features: - Interactive code playground with simple, age-appropriate examples - Visual representations of program flow and execution - Step-by-step demonstrations of each programming concept - Guided exercises with immediate feedback - Teacher notes and explanatory comments for classroom use - Sample programs demonstrating real-world applications UI/Style: - Playful, education-focused interface with cartoon-style programming elements - Color-coded blocks and visual cues to distinguish different programming concepts - Kid-friendly animations that illustrate code execution and program flow - Interactive elements that respond to student interactions with encouraging feedback submitted by /u/coffeechug [link] [comments]

• Discussions of Future AI Impact?
  by /u/notgalgon (Artificial Intelligence Gateway) on December 6, 2024 at 7:12 pm

  Are there any blogs, books, podcasts, papers, forums or whatever, that have discussions on how the world will change overtime with AI progress? I am curious to hear from people who have thought deeply on the subject and into the different aspects of work/life. Not just that the next model will do X but how more like how will the food industry be impacted. What types of jobs will be automated away soon, what new types of jobs will be created, what things in the home will be impacted first, etc. Obviously no one can accurately predict what will happen but i would be interested in reading/listening to educated guesses on it. submitted by /u/notgalgon [link] [comments]