#Conversation intelligence software

[anushapranu]

🎧 𝐔𝐧𝐥𝐨𝐜𝐤𝐢𝐧𝐠 𝐒𝐚𝐥𝐞𝐬 𝐄𝐱𝐜𝐞𝐥𝐥𝐞𝐧𝐜𝐞: 𝐓𝐡𝐞 𝐑𝐢𝐬𝐞 𝐨𝐟 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞

 Conversation Intelligence Software Market Size (2025 – 2030)

The Conversation Intelligence Software Market witnessed a valuation of USD 9.47 Billion in 2024, with projections indicating a substantial increase to reach USD 43.03 Billion by 2030, driven by a robust compound annual growth rate (CAGR) of 24.14% during the forecast period spanning from 2025 to 2030.

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CONVERSATION

Conversation Intelligence Software leverages advanced technology to enable organizations to analyze and optimize the efficacy of their customer interactions, encompassing phone calls and chat conversations. By harnessing Artificial Intelligence (AI) and Natural Language Processing (NLP), this software facilitates the identification, evaluation, and elucidation of insights from customer engagements, thereby elevating levels of customer service, revenue generation, and compliance. Moreover, the market for conversation intelligence software is witnessing rapid expansion, propelled by heightened demand for superior customer service and engagement, alongside the imperative for more effective sales and marketing strategies. Advancements in Machine Learning (ML) and Natural Language Processing (NLP) further fuel this market growth, as they simplify the automation and enhancement of conversational interactions with customers for businesses. The accelerated adoption of conversation intelligence software is also attributed to the surge in remote work setups and the transition towards online channels for customer engagement. As industries increasingly invest in these technologies to fortify their capabilities in customer engagement and support, the conversation intelligence software market is poised for sustained growth in the foreseeable future.

Global Conversation Intelligence Software Market Drivers:

The Conversation Intelligence Software Market is primarily propelled by the escalating preference for cloud-based solutions and the growing demand for automating customer service processes.

Businesses are inclined towards automating their customer service operations to streamline costs and enhance operational efficiency. Conversation intelligence software facilitates this objective by furnishing insights into customer interactions, thereby empowering businesses to optimize customer service, sales, and compliance efforts. Additionally, the burgeoning adoption of cloud-based solutions significantly contributes to market expansion. Cloud-based conversation intelligence software offers cost-effective alternatives to on-premises solutions, with simplified implementation and maintenance processes. Moreover, cloud-based solutions offer scalability and flexibility, essential attributes for businesses eyeing expansion opportunities. Consequently, the Conversation Intelligence Software Market is witnessing robust growth driven by the increasing traction towards cloud-based solutions and the burgeoning demand for automating customer service processes. This presents businesses and technology providers with opportunities to augment their operations and innovate novel solutions, leveraging this technology.

Conversation intelligence software leverages AI and ML to enhance customer comprehension, marketing effectiveness, and service efficiency.

The proliferation of AI and ML methodologies underpins the growth of the conversation intelligence software market. These technologies facilitate more precise and efficient analysis of client interactions, enhancing comprehension of customer needs and preferences. This, in turn, could catalyze more targeted and personalized marketing campaigns, alongside delivering tailored customer services. Furthermore, the integration of AI and ML capabilities enables the automation of numerous manual customer service tasks, leading to cost savings and heightened business efficiency. As long as organizations continue to invest in these technologies, the demand for conversation intelligence software is poised to remain robust.

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Global Conversation Intelligence Software Market Challenges:

For some businesses, the upfront costs associated with implementing and maintaining conversation intelligence software pose a challenge. Expenses related to software acquisition, alongside ongoing costs for data storage, updates, and technical support, may act as deterrents. However, the penetration of conversational intelligence software is hindered by the requisite for skilled professionals to manage it and a knowledge gap surrounding its functionalities. With businesses prioritizing the delivery of top-notch digital experiences and customers seeking avenues for their voices to be heard, conversational intelligence software empowers companies to deliver superior customer experiences and respond swiftly and effectively to intricate queries.

Global Conversation Intelligence Software Market Opportunities:

The global Conversation Intelligence Software Market presents an array of opportunities:

Substantial opportunities abound for companies and technology providers within the Conversation Intelligence Software Market as businesses increasingly seek to automate customer service processes and enhance customer interactions. This anticipated surge in demand for conversation intelligence software underscores the need for technology providers to innovate and deliver solutions catering to diverse industry verticals.

Businesses stand to gain significant improvements in sales and customer service by leveraging conversation intelligence software. By analyzing customer interactions to glean insights into customer needs and preferences, businesses can refine their customer service and sales strategies. Additionally, businesses can utilize conversation intelligence software to proactively identify potential issues and areas for enhancement in their customer interactions, thereby enhancing compliance efforts.

Another opportunity for businesses lies in leveraging conversation intelligence software to drive cost savings. By automating certain customer service tasks, businesses can reduce labor costs and boost productivity. Cloud-based conversation intelligence software, in particular, offers a cost-effective alternative to on-premises infrastructure, enabling businesses to realize significant cost savings.

Technology vendors have the opportunity to develop and market cutting-edge solutions tailored to meet the evolving needs of businesses across various sectors, fueled by market demand. Anticipated market expansion in the ensuing years suggests heightened demand for technology products and services. Moreover, technology vendors must continually innovate to maintain a competitive edge over rivals and deliver novel functionalities and capabilities.

COVID-19 Impact on Global Conversation Intelligence Software Market:

The advent of COVID-19 has left an indelible mark on the Conversation Intelligence Software Market, accelerating the adoption of conversational AI technologies. As businesses and organizations transitioned to remote work setups and digital channels for customer and employee engagements, the demand for digital technologies like conversational intelligence software surged. Industries spanning healthcare, retail, e-commerce, and telecommunications witnessed rapid adoption of chatbots and other conversational intelligence software solutions, enabling them to sustain customer service and support amid the pandemic.

Global Conversation Intelligence Software Market Recent Developments:

In April 2022, Microsoft and BPCL forged a partnership aimed at transforming the client experience through the amalgamation of cloud technology and artificial intelligence. Leveraging administrative specialists equipped with conversational AI capabilities, BPCL facilitated personalized assistance to clients, thereby enhancing the client experience.

In November 2022, OpenAI unveiled the new AI model ChatGPT, designed to replicate human-like conversations. ChatGPT leverages OpenAI's ChatGPT conversational AI tool, built upon the foundation of the OpenAI GPT-2 model, a robust unsupervised natural language generation model.

Segmentation of the Conversation Intelligence Software Market by Type:

ChatBots

Intelligent Virtual Assistant (IVA)

Throughout the projected period, the ChatBots sector is expected to maintain its dominance, commanding a significant share of over 65%. This is attributed to its ability to efficiently provide essential customer support, enabling customers to inquire, schedule appointments, and seek clarifications regarding various products or services. Given their capability to handle numerous customer inquiries effectively, chatbots have emerged as a popular tool for customer service, especially in dealing with routine tasks.

Moreover, many businesses offering conversational intelligence software services develop customizable chatbots and virtual assistants with limited features. Due to their proficiency in understanding and responding to customer queries in natural language, chatbots are deemed cost-effective solutions for customer service needs. Their widespread adoption across various industries and business sizes is driven by their efficiency in handling routine tasks and providing prompt customer service.

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Global Segmentation of Conversation Intelligence Software Market by Technology:

Natural Language Processing (NLP)

Machine Learning (ML)

Deep Learning

During the forecast period, the Machine Learning segment is projected to dominate the market. Machine Learning, a subset of Artificial Intelligence (AI), enables software to learn from data without explicit programming. Its ability to analyze large datasets and make predictions or decisions makes it a preferred choice across various industries, particularly in retail, where it has revolutionized customer experiences, product development, and supply chain operations. Many retail tech firms are investing in machine learning to build AI platforms.

Furthermore, the Natural Language Processing (NLP) market is expected to experience the fastest growth. NLP, an AI subset, focuses on the interaction between computers and human language. By integrating NLP with technologies like chatbots and voice assistants, conversational AI creates interfaces for voice or chat communication, enabling more human-like interactions and assistance to customers.

Global Segmentation of Conversation Intelligence Software Market by End User:

IT & Telecom

BFSI

Retail & E-Commerce

Healthcare

Education

Government

Automotive

Others

Retail and e-commerce industries employ conversational intelligence tools to enhance customer service, providing more effective, expressive, and intelligent support. Significant growth is anticipated in the BFSI sector during the forecast period. AI-powered chatbots are increasingly utilized in financial services, simplifying secure transactions and communication while aiding in fraud detection and managing internal operations.

In the automotive sector, AI voice assistants enhance the commuting experience by providing vehicle controls, alerts, real-time recommendations, and more.

Global Segmentation of Conversation Intelligence Software Market by Region:

North America

Europe

Asia Pacific

Middle East

Latin America

North America is expected to dominate the conversation intelligence software market during the forecast period. Key corporations and regulatory bodies in the region are heavily investing in AI-based technologies across manufacturing, BFSI, healthcare, and retail sectors. The market is driven by the increasing demand for AI-powered customer service solutions and rapid adoption of advanced technology. Within North America, the US is projected to lead, supported by its significant presence of AI players.

The Asia-Pacific region is expected to witness the fastest growth in the conversation intelligence software market. Factors such as the rapid expansion of e-commerce, increased internet penetration, technological advancements in retail and healthcare, and rising demand for consulting services are driving the adoption of conversation intelligence software. China is poised to be the largest and fastest-growing market in the Asia-Pacific region.

Key Players in the Global Conversation Intelligence Software Market:

OpenAI

Microsoft

IBM

Oracle

Kore.Ai

Avaamo

Jio Haptik Technologies Ltd.

Rasa Technologies Inc.

Amazon Web Services

Google

[precallai]

Integrating AI Call Transcription into Your VoIP or CRM System

In today’s hyper-connected business environment, customer communication is one of the most valuable assets a company possesses. Every sales call, support ticket, or service request contains rich data that can improve business processes—if captured and analyzed properly. This is where AI call transcription becomes a game changer. By converting voice conversations into searchable, structured text, businesses can unlock powerful insights. The real value, however, comes when these capabilities are integrated directly into VoIP and CRM systems, streamlining operations and enhancing customer experiences.

Why AI Call Transcription Matters

AI call transcription leverages advanced technologies such as Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) to convert real-time or recorded voice conversations into text. These transcripts can then be used for:

Compliance and auditing

Agent performance evaluation

Customer sentiment analysis

CRM data enrichment

Automated note-taking

Keyword tracking and lead scoring

Traditionally, analyzing calls was a manual and time-consuming task. AI makes this process scalable and real-time.

Key Components of AI Call Transcription Systems

Before diving into integration, it’s essential to understand the key components of an AI transcription pipeline:

Speech-to-Text Engine (ASR): Converts audio to raw text.

Speaker Diarization: Identifies and separates different speakers.

Timestamping: Tags text with time information for playback syncing.

Language Modeling: Uses NLP to enhance context, punctuation, and accuracy.

Post-processing Modules: Cleans up the transcript for readability.

APIs/SDKs: Interface for integration with external systems like CRMs or VoIP platforms.

Common Use Cases for VoIP + CRM + AI Transcription

The integration of AI transcription with VoIP and CRM platforms opens up a wide range of operational enhancements:

Sales teams: Automatically log conversations, extract deal-related data, and trigger follow-up tasks.

Customer support: Analyze tone, keywords, and escalation patterns for better agent training.

Compliance teams: Use searchable transcripts to verify adherence to legal and regulatory requirements.

Marketing teams: Mine conversation data for campaign insights, objections, and buying signals.

Step-by-Step: Integrating AI Call Transcription into VoIP Systems

Step 1: Capture the Audio Stream

Most modern VoIP systems like Twilio, RingCentral, Zoom Phone, or Aircall provide APIs or webhooks that allow you to:

Record calls in real time

Access audio streams post-call

Configure cloud storage for call files (MP3, WAV)

Ensure that you're adhering to legal and privacy regulations such as GDPR or HIPAA when capturing and storing call data.

Step 2: Choose an AI Transcription Provider

Several commercial and open-source options exist, including:

Google Speech-to-Text

AWS Transcribe

Microsoft Azure Speech

AssemblyAI

Deepgram

Whisper by OpenAI (open-source)

When selecting a provider, evaluate:

Language support

Real-time vs. batch processing capabilities

Accuracy in noisy environments

Speaker diarization support

API response latency

Security/compliance features

Step 3: Transcribe the Audio

Using the API of your chosen ASR provider, submit the call recording. Many platforms allow streaming input for real-time use cases, or you can upload an audio file for asynchronous transcription.

Here’s a basic flow using an API:

python

CopyEdit

import requests

response = requests.post(

    "https://api.transcriptionprovider.com/v1/transcribe",

    headers={"Authorization": "Bearer YOUR_API_KEY"},

    json={"audio_url": "https://storage.yourvoip.com/call123.wav"}

)

transcript = response.json()

The returned transcript typically includes speaker turns, timestamps, and a confidence score.

Step-by-Step: Integrating Transcription with CRM Systems

Once you’ve obtained the transcription, you can inject it into your CRM platform (e.g., Salesforce, HubSpot, Zoho, GoHighLevel) using their APIs.

Step 4: Map Transcripts to CRM Records

You’ll need to determine where and how transcripts should appear in your CRM:

Contact record timeline

Activity or task notes

Custom transcription field

Opportunity or deal notes

For example, in HubSpot:

python

CopyEdit

requests.post(

    "https://api.hubapi.com/engagements/v1/engagements",

    headers={"Authorization": "Bearer YOUR_HUBSPOT_TOKEN"},

    json={

        "engagement": {"active": True, "type": "NOTE"},

        "associations": {"contactIds": [contact_id]},

        "metadata": {"body": transcript_text}

    }

)

Step 5: Automate Trigger-Based Actions

You can automate workflows based on keywords or intent in the transcript, such as:

Create follow-up tasks if "schedule demo" is mentioned

Alert a manager if "cancel account" is detected

Move deal stage if certain intent phrases are spoken

This is where NLP tagging or intent classification models can add value.

Advanced Features and Enhancements

1. Sentiment Analysis

Apply sentiment models to gauge caller mood and flag negative experiences for review.

2. Custom Vocabulary

Teach the transcription engine brand-specific terms, product names, or industry jargon for better accuracy.

3. Voice Biometrics

Authenticate speakers based on voiceprints for added security.

4. Real-Time Transcription

Show live captions during calls or video meetings for accessibility and note-taking.

Challenges to Consider

Privacy & Consent: Ensure callers are aware that calls are recorded and transcribed.

Data Storage: Securely store transcripts, especially when handling sensitive data.

Accuracy Limitations: Background noise, accents, or low-quality audio can degrade results.

System Compatibility: Some CRMs may require custom middleware or third-party plugins for integration.

Tools That Make It Easy

Zapier/Integromat: For non-developers to connect transcription services with CRMs.

Webhooks: Trigger events based on call status or new transcriptions.

CRM Plugins: Some platforms offer native transcription integrations.

Final Thoughts

Integrating AI call transcription into your VoIP and CRM systems can significantly boost your team’s productivity, improve customer relationships, and offer new layers of business intelligence. As the technology matures and becomes more accessible, now is the right time to embrace it.

With the right strategy and tools in place, what used to be fleeting conversations can now become a core part of your data-driven decision-making process.

[innovatehub-techtalk]

Welcome to InnovateHub TechTalk: Unleashing the Tech Frontier

Greetings, fellow tech enthusiasts, and welcome to the inaugural edition of InnovateHub TechTalk! I am Lucas Redford, your guide on this thrilling expedition into the boundless realms of technology. With each keystroke and pixel, we'll embark on a journey to unravel the mysteries, embrace the innovations, and discuss the trends that shape our digital world.

Charting New Horizons:

In the age of rapid technological advancement, it's impossible to ignore the transformative impact that technology has on our lives. From the moment we wake up to the time we rest our heads, technology surrounds us, empowering, entertaining, and evolving at an unprecedented pace.

Our Quest:

At InnovateHub TechTalk, our mission is simple yet profound: to ignite your curiosity and keep you informed about the dynamic world of technology. Whether you're a seasoned coder, a budding entrepreneur, a digital artist, or just someone intrigued by the possibilities, this platform is your haven.

What Awaits You:

As we embark on this voyage together, here's a glimpse of what you can expect from InnovateHub TechTalk:

Innovative Spotlights: Venture into the heart of innovation as we showcase groundbreaking technologies and inventions that are reshaping industries and society.

Tech Chats with Experts: Join me in engaging conversations with thought leaders, industry experts, and visionaries who are shaping the course of technology.

CodeCraft Corner: Whether you're a coding novice or a seasoned pro, our CodeCraft Corner will be your source for coding tips, projects, and insights to elevate your programming prowess.

FutureTalk: Delve into the crystal ball as we discuss emerging trends, speculative tech, and the potential future landscapes that await us.

Be a Part of the Conversation:

InnovateHub TechTalk is not just a blog; it's a community. Your thoughts, questions, and insights are the catalysts that will drive our discussions forward. Don't hesitate to jump into the comment section, share your perspectives, and connect with fellow tech aficionados.

With great excitement, I invite you to journey with me through the digital maze, the electronic wonderland, and the data-driven universe that defines our age. Together, we'll decode complexities, celebrate achievements, and ponder the limitless possibilities that lie ahead.

As we dive into the sea of 1s and 0s, remember that innovation knows no bounds, and at InnovateHub TechTalk, we're poised to explore it all.

Welcome aboard, tech voyagers!

Lucas Redford

Founder and Chief Explorer, InnovateHub TechTalk

[codestudiopak]

10 Best Conversion Rate Optimization Tools in 2025

In the fast-evolving digital world of 2025, driving traffic to your website is only half the battle. Converting that traffic into loyal customers is where the real challenge and opportunity lies. Conversion Rate Optimization (CRO) tools are the secret weapons businesses use to turn casual visitors into engaged buyers. Whether you’re struggling with high bounce rates, low form submissions or abandoned carts, the right CRO tools can solve these problems while educating you on user behavior and optimization strategies.

[rohitpalan]

Conversation Intelligence Software Market Soars to USD 22.8 Billion in 2023, Expected to Double by 2033

The conversation intelligence software market is registering a valuation of US$ 22.8 billion in 2023 and is projected to reach US$ 46.8 billion by 2033. The market is securing a CAGR of 7.4% during the forecast period.

Request a Sample of this Report  https://www.futuremarketinsights.com/reports/sample/rep-gb-14518

How is the Popularity of Conversation Intelligence Software Growing?

Conversation intelligence software is gaining huge popularity in enhancing consumers’ interaction experiences. A few of the factors that attribute the growth are:

Consumers Experience:The rising demand for conversation intelligence software focus on delivering consumer needs, analyzing better consumer interactions, and improving services. Several businesses are increasing the adoption of this software to enhance consumer satisfaction.

Sales and Marketing: Conversation intelligence software is widely used to help marketing and sales by optimizing strategies efficiently. It analyzes demos, sales calls, and consumer interactions with enhancing marketing campaigns.

Performance Tracking and Coaching:Conversation intelligence software supervises, tract, and monitors employees’ performance. It analyzes actionable feedback and conversations and improves efficiency. It benefits the sales team, call centers, and customer support departments.

Compliance and Risk Management:The end-use industries such as healthcare, finance, and telecommunications play a vital role in expanding the global market. Conversation intelligence solutions identify potential risks and regulatory complaints and ensure industry guidelines and standards.

Advancements in Artificial Intelligence and Natural Language Processing:The growing advanced technologies are improving capabilities in conversation intelligence software. These software tools can analyze accurate reports, such as detecting sentiment, identifying keywords, and providing real-time monitoring.

Key Takeaways: 

The conversation intelligence software market is estimated to secure a CAGR of 7.4% with a valuation of US$ 46.8 billion by 2033.

In the historic period, the market secured a valuation of US$ 17.5 billion with a CAGR of 5.3% in 2018.

The United States is dominating the global market by capturing a maximum share of 17.6% by 2033.

The United States is anticipated to register a CAGR of 6.4% in the global market by 2033.

How Key Vendors are Developing Strategic Plans to Upsurge the Global Market?

The number of key players in the regions highly fragments the market. These players play a key role in expanding the global market by investing huge amounts in research and developing activities to build improved products. Through these activities, they also innovate advanced products to attract consumers and collect huge revenue.

Key players are adopting various marketing strategies to uplift the global market, which include mergers, collaborations, acquisitions, product launches, and agreements. These players continue to flourish in the global market through their unique and newly launched products as per consumers’ requirements during the forecast period.

Recent Developments in the Global Market:

In 2021, Gong.io announced that it raised funding to enhance its business portfolio in the salesforce training platform.

ai offers artificial intelligence to enhance sales conversations, improve coaching and provide insights. The company integrated the popular conversation tool Zoom.

In 2021, CallRail announced its new call tracking analytics to help businesses to improve customer services by analyzing phone calls, tract campaigns, and marketing.

ai offers sales coaching that helps managers to identify coaching opportunities, provide feedback, track team progress, and monitor.

Conversation Intelligence Software Market by Category

By Deployment:

On-premise

Cloud-based

By End Users:

SMEs

Large Enterprises

By Vertical:

IT & Telecommunications

Retail

BFSI

Real Estate

Other Verticals

By Region:

North America

Latin America

Europe

Asia Pacific

The Middle East and Africa

[sunsmartglobal-blog]

Revolutionizing Banking with Customer Service Chatbots

Long Wait Times: Customers often face lengthy wait times when trying to reach a bank's customer service, leading to frustration and dissatisfaction. Limited Availability: Traditional customer service is typically available during business hours, leaving customers without support during evenings, weekends, and holidays. Repetitive Queries: Banks receive a high volume of repetitive queries, such as account balances, transaction history, and branch locations, which can be time-consuming for human agents to handle. Language Barriers: Customers who speak languages other than the bank's primary language may struggle to communicate effectively with customer service representatives. Lack of Personalization: Generic responses from customer service agents can make customers feel like they are not being heard or understood. Inefficient Processes: Manual processes and outdated systems can slow down the resolution of customer issues, leading to delays and dissatisfaction. Increasing Costs: Maintaining a large customer service team can be costly for banks, especially as the demand for support continues to grow.

WhatsApp link: https://wa.link/g2d5fw Mail: sales@herbie.ai Website: www.herbie.ai | https://www.herbie.ai/industries/banking/

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[chatmetrics]

Live Chat: The Game-Changer for Your Business

In today’s fast-paced digital landscape, businesses are constantly seeking innovative ways to engage with their customers and enhance the overall customer experience. One such tool that has emerged as a game-changer in the realm of customer service is live chat. This article explores the various aspects of live chat and how it can significantly impact your business.

Introduction to Live Chat

In the ever-evolving world of customer service, live chat has emerged as a vital communication channel between businesses and their customers. Unlike traditional methods such as email or phone support, live chat offers real-time assistance, allowing businesses to address customer queries and concerns promptly.

Benefits of Live Chat for Businesses

Real-time Assistance

Live chat enables businesses to provide immediate support to customers, eliminating the need for lengthy wait times associated with traditional customer service channels. This real-time assistance can lead to higher customer satisfaction levels and improved retention rates.

Increased Customer Satisfaction

By offering instant support and resolving issues in a timely manner, businesses can significantly enhance the overall customer experience. Customers appreciate the convenience of live chat, as it allows them to communicate with businesses effortlessly without having to pick up the phone or compose lengthy emails.

Cost-effectiveness

Compared to traditional customer service channels such as phone support, live chat is a cost-effective solution for businesses. It allows support agents to handle multiple chats simultaneously, reducing the need for additional staffing and resources.

Implementation of Live Chat

Implementing live chat requires careful planning and consideration. Businesses must choose the right live chat platform that aligns with their needs and requirements. Additionally, staff members must be adequately trained to provide efficient and effective live chat support.

Strategies for Effective Live Chat

To maximize the benefits of live chat, businesses should employ various strategies for effective customer engagement. This includes ensuring prompt responses to customer inquiries, personalizing interactions based on customer preferences, and developing strong problem-solving skills to address complex issues.

Live Chat Metrics and Analytics

Monitoring key metrics and analytics is essential for evaluating the performance of live chat support. Businesses should track metrics such as chat volume, response times, and customer satisfaction scores to identify areas for improvement and optimize the overall customer experience.

Case Studies of Successful Implementation

Numerous businesses across various industries have successfully implemented live chat as part of their customer service strategy. From e-commerce companies to service-based industries, the benefits of live chat are evident in improved customer satisfaction and increased sales.

Challenges and Solutions

While live chat offers numerous benefits, it also presents challenges for businesses, such as handling multiple chats simultaneously and dealing with difficult customers. However, with proper training and the right tools in place, businesses can overcome these challenges and deliver exceptional customer service.

Future Trends in Live Chat

Looking ahead, the future of live chat is ripe with exciting possibilities. Advancements in artificial intelligence (AI) and automation are poised to revolutionize the way businesses engage with customers through live chat, offering enhanced personalization and efficiency.

Conclusion

In conclusion, live chat has emerged as a game-changer for businesses looking to elevate their customer service efforts. By providing real-time assistance, increasing customer satisfaction, and offering a cost-effective solution, live chat has become an indispensable tool for modern businesses.

Unique FAQs

What types of businesses can benefit from implementing live chat?

Virtually any business that interacts with customers online can benefit from live chat, including e-commerce stores, SaaS companies, and service-based industries.

How can businesses measure the success of their live chat support?

Businesses can measure the success of their live chat support by tracking metrics such as chat volume, response times, and customer satisfaction scores.

Are there any downsides to using live chat for customer support?

While live chat offers numerous benefits, it can present challenges such as handling multiple chats simultaneously and dealing with difficult customers. However, with proper training and resources, these challenges can be overcome.

What are some best practices for training staff for live chat support?

Best practices for training staff for live chat support include providing comprehensive product knowledge, teaching effective communication skills, and offering ongoing support and feedback.

What role will artificial intelligence play in the future of live chat?

Artificial intelligence is expected to play a significant role in the future of live chat, with advancements in AI technology enabling businesses to deliver more personalized and efficient customer experiences.

[intelvueofficial]

The Future of AI: Predictions and Trends

Introduction

Artificial Intelligence (AI) has been at the forefront of recent technological developments, which have been occurring at an unprecedented rate. Several significant trends that promise to transform how we use technology, conduct business, and even market goods and services are emerging as we go into the future. With an emphasis on the Metaverse, the most important technological trends, and the incorporation of AI in digital marketing, this article will look into the predictions and trends that are influencing the future of AI.

Metaverse Trends: The Convergence of Reality and Digital Worlds

In recent years, there has been a lot of discussion and excitement surrounding the idea of the Metaverse trends, an entirely immersive virtual universe. The Metaverse, which is envisioned as a shared, persistent area where people engage with one other and a computer-generated environment, has the potential to fundamentally alter how we engage in social relationships, entertainment, work, and commerce.

Virtual Reality (VR) and Augmented Reality (AR):

Technology advancements in VR and AR are allowing for more immersive experiences. In the upcoming years, we may anticipate more affordable and realistic VR and AR experiences thanks to technology and software developments.

These technologies are not just used for gaming and amusement; they are also being used for remote business, training, and even education. The possibilities are endless; consider attending a conference in a virtual boardroom or touring historical places with augmented reality overlays.

Blockchain and NFTs:

The Metaverse is made possible by blockchain technology  and Non-Fungible Tokens (NFTs), which enable digital ownership, authenticity, and safe transactions in virtual settings. As more blockchain and NFT applications are researched, this tendency is expected to persist.

The purchasing, selling, and ownership of digital assets is about to undergo a transformation thanks in part to NFTs. NFTs offer a framework for actual digital ownership, establishing the groundwork for a vibrant economy within the Metaverse. This includes virtual real estate and one-of-a-kind works of digital art.

AI-driven Personalization:

The Metaverse will become increasingly personalized thanks in large part to AI algorithms. With personalized content recommendations and dynamically produced surroundings, AI will increase user pleasure and engagement.

Imagine entering a virtual world where everything, from the climate to the architecture, adapts to your preferences. This level of customisation will be made possible by AI, resulting in a completely distinctive experience for each user.

Top Technology Trends: A Glimpse into Tomorrow's Innovations

Beyond the Metaverse, AI is poised to drive several other groundbreaking technology trends that will shape our future.

Quantum Computing:

Quantum computing is expected to completely change processing power as Moore's Law reaches its limits. Quantum computing has the potential to resolve issues that were formerly thought to be intractable due to its capacity to carry out complicated calculations at rates that are currently unfathomable.

The potential of quantum computing are enormous, from transforming drug discovery to streamlining intricate logistical processes. Even though we're still in the early phases, it's exciting to think about the possibility of finding solutions to today's seemingly insoluble issues.

Edge Computing:

Data processing and storage are moved closer to the point of data generation thanks to edge computing. The demand for real-time processing and decreased latency, which are essential for applications like driverless vehicles, IoT devices, and augmented reality, is what is driving this development.

Applications that need the capacity to make split-second decisions now have more options thanks to the ability to process data at the edge. For instance, autonomous vehicles use edge computing to interpret sensory data and make quick driving judgments.

AI-powered Healthcare:

AI will have a significant impact on healthcare, from individualized treatment plans to drug development. Large datasets will be analyzed by machine learning algorithms to produce more precise diagnoses and treatment alternatives.

Patient care is being transformed by AI's capacity to sift through masses of medical data to find patterns and insights. AI is revolutionizing the healthcare industry by doing everything from forecasting disease outbreaks to developing individualized treatment plans.

AI in Digital Marketing: Elevating Customer Experiences

AI is already changing the game in the field of digital marketing, and this trend is only going to continue.

Predictive Analytics and Customer Segmentation:

Predictive analytics powered by AI can anticipate consumer behavior and preferences, allowing businesses to target their marketing campaigns at particular groups.

Businesses may forecast customer requirements and behaviors using historical data and machine learning algorithms, enabling more specialized and successful marketing efforts. As a result, conversion rates increase and marketing resources are used more effectively.

 

Chatbots and Virtual Assistants:

Virtual assistants and chatbots powered by AI are getting increasingly smart, offering prompt responses and tailored interactions. They improve customer interaction and support, which eventually results in improved conversion rates.

Because they can now comprehend context and real language, chatbots are invaluable for delivering prompt support. They are accessible round-the-clock, giving clients prompt responses while freeing up human agents for more difficult jobs.

Visual Recognition and Content Creation:

Images and films can now be recognized and created by AI systems. This creates new opportunities for content development and gives users the option to have their images customized.

Visual recognition enables automated image tagging and categorization, simplifying the organization and search of big image datasets. AI-generated content can also help in the creation of visually appealing marketing materials.

Conclusion

AI is poised to change many parts of our life in the future, including how we interact in virtual environments and the technological advancements that will influence our environment. The possibilities appear endless as the Metaverse picks up steam and technological trends like edge computing and quantum computing continue to develop. AI will keep improving customer experiences in the field of digital marketing, personalizing and engaging interactions. Being on the cutting edge of these technical developments is exhilarating because we get to see a future that only seemed possible in science fiction come to life. With AI at the lead, innovation's potential is limitless, offering a more connected, intelligent, and dynamic future than ever.

[intelvue]

How to Create a Chatbot: [A Step-by-Step Guide 2023]

🤖✨ Dive into 2023 with our Step-by-Step Guide on Creating a Chatbot! Elevate your digital presence with this game-changing tech. Read More: https://www.intelvue.com/how-to-create-chatbot/

[xtruss]

This picture taken on January 23, 2023 in Toulouse, Southwestern France, shows screens displaying the logos of OpenAI and ChatGPT. — ChatGPT is a Conversational Artificial Intelligence Software Application Developed By OpenAI. Lionel Bonaventure/AFP Via Getty Images

Opinion: Want Protection From AI? The First Step Is a National Privacy Law

— By Suzan K. DelBene, Democratic Congresswoman Washington | August 28, 2023

In the six months since a new chatbot confessed its love for a reporter before taking a darker turn, the world has woken up to how artificial intelligence can dramatically change our lives—and how it can go awry. AI is quickly being integrated into nearly every aspect of our economy and daily lives. Yet in our nation's capital, laws aren't keeping up with the rapid evolution of technology.

Policymakers have many decisions to make around artificial intelligence, like how it can be used in sensitive areas such as financial markets, health care, and national security. They will need to decide intellectual property rights around AI-created content. There will also need to be guardrails to prevent the dissemination of mis- and disinformation.

But before we build the second and third story of this regulatory house, we need to lay a strong foundation and that must center around a national data privacy standard.

To understand this bedrock need, it's important to look at how artificial intelligence was developed. AI needs an immense quantity of data. The generative language tool ChatGPT was trained on 45 terabytes of data, or the equivalent of over 200 days' worth of HD video. That information may have included our posts on social media and online forums that have likely taught ChatGPT how we write and communicate with each other. That's because this data is largely unprotected and widely available to third-party companies willing to pay for it. AI developers do not need to disclose where they get their input data from because the U.S. has no national privacy law.

While data studies have existed for centuries and can have major benefits, they are often centered around consent to use that information. Medical studies often use patient health data and outcomes, but that information needs the approval of the study participants in most cases. That's because in the 1990s, Congress gave health information a basic level of protection, but that law only protects data shared between patients and their health care providers. The same is not true for other health platforms like fitness apps, or most other data we generate today, including our conversations online and geolocation information.

Currently, the companies that collect our data are in control of it. Google for years scanned Gmail inboxes to sell users targeted ads, before abandoning the practice. Zoom recently had to update its data collection policy after it was accused of using customers' audio and video to train its AI products. We've all downloaded an app on our phone and immediately accepted the terms and conditions window without actually reading it. Companies can and often do change the terms regarding how much of our information they collect and how they use it.

A national privacy standard would ensure a baseline set of protections, no matter where someone lives in the U.S. And it would restrict companies from storing and selling our personal data.

Ensuring there's transparency and accountability in what data goes into AI is also important for a quality and responsible product. If input data is biased, we're going to get a biased outcome, in other words, "garbage in, garbage out." Facial recognition is one application of artificial intelligence. These systems have by and large been trained by and with data from white people. That's led to clear biases when communities of color interact with this technology.

The United States must be a global leader on artificial intelligence policy.

But other countries are not waiting as we sit still. The European Union has moved faster on AI regulations, because it passed its privacy law in 2018. The Chinese government has also moved quickly on AI, though in an alarmingly anti-democratic way. If we want a seat at the international table to set the long-term direction for AI that reflects our core American values, we must have our own national data privacy law to start.

The Biden administration has taken some encouraging steps to begin putting guardrails around AI, but it has been constrained by Congress' inaction. The White House recently announced voluntary artificial intelligence standards, which include a section on data privacy. Voluntary guidelines don't come with accountability, and the federal government can only enforce the rules on the books, which are woefully outdated.

That's why Congress needs to step up and set the rules of the road. Strong national standards like privacy must be uniform throughout the country, rather than the state-by-state approach we have now. It has to put people back in control of their information instead of companies. It must also be enforceable so that the government can hold bad actors accountable.

These are the components of the legislation I have introduced over the past few Congresses and the bipartisan proposal the Energy & Commerce Committee advanced last year.

As with all things in Congress, it comes down to a matter of priorities. With artificial intelligence expanding so fast, we can no longer wait to take up this issue.

We were behind on technology policy already, but we are falling further behind as other countries take the lead. We must act quickly and set a robust foundation. That has to include a strong, enforceable national privacy standard.

— Congresswoman Suzan K. DelBene represents Washington's 1st District in the United States House of Representatives.

[faultfalha]

Nvidia has announced the availability of DGX Cloud on Oracle Cloud Infrastructure. DGX Cloud is a fast, easy and secure way to deploy deep learning and AI applications. It is the first fully integrated, end-to-end AI platform that provides everything you need to train and deploy your applications.

[phantomrose96]

The conversation around AI is going to get away from us quickly because people lack the language to distinguish types of AI--and it's not their fault. Companies love to slap "AI" on anything they believe can pass for something "intelligent" a computer program is doing. And this muddies the waters when people want to talk about AI when the exact same word covers a wide umbrella and they themselves don't know how to qualify the distinctions within.

I'm a software engineer and not a data scientist, so I'm not exactly at the level of domain expert. But I work with data scientists, and I have at least rudimentary college-level knowledge of machine learning and linear algebra from my CS degree. So I want to give some quick guidance.

What is AI? And what is not AI?

So what's the difference between just a computer program, and an "AI" program? Computers can do a lot of smart things, and companies love the idea of calling anything that seems smart enough "AI", but industry-wise the question of "how smart" a program is has nothing to do with whether it is AI.

A regular, non-AI computer program is procedural, and rigidly defined. I could "program" traffic light behavior that essentially goes { if(light === green) { go(); } else { stop();} }. I've told it in simple and rigid terms what condition to check, and how to behave based on that check. (A better program would have a lot more to check for, like signs and road conditions and pedestrians in the street, and those things will still need to be spelled out.)

An AI traffic light behavior is generated by machine-learning, which simplistically is a huge cranking machine of linear algebra which you feed training data into and it "learns" from. By "learning" I mean it's developing a complex and opaque model of parameters to fit the training data (but not over-fit). In this case the training data probably includes thousands of videos of car behavior at traffic intersections. Through parameter tweaking and model adjustment, data scientists will turn this crank over and over adjusting it to create something which, in very opaque terms, has developed a model that will guess the right behavioral output for any future scenario.

A well-trained model would be fed a green light and know to go, and a red light and know to stop, and 'green but there's a kid in the road' and know to stop. A very very well-trained model can probably do this better than my program above, because it has the capacity to be more adaptive than my rigidly-defined thing if the rigidly-defined program is missing some considerations. But if the AI model makes a wrong choice, it is significantly harder to trace down why exactly it did that.

Because again, the reason it's making this decision may be very opaque. It's like engineering a very specific plinko machine which gets tweaked to be very good at taking a road input and giving the right output. But like if that plinko machine contained millions of pegs and none of them necessarily correlated to anything to do with the road. There's possibly no "if green, go, else stop" to look for. (Maybe there is, for traffic light specifically as that is intentionally very simplistic. But a model trained to recognize written numbers for example likely contains no parameters at all that you could map to ideas a human has like "look for a rigid line in the number". The parameters may be all, to humans, meaningless.)

So, that's basics. Here are some categories of things which get called AI:

"AI" which is just genuinely not AI

There's plenty of software that follows a normal, procedural program defined rigidly, with no linear algebra model training, that companies would love to brand as "AI" because it sounds cool.

Something like motion detection/tracking might be sold as artificially intelligent. But under the covers that can be done as simply as "if some range of pixels changes color by a certain amount, flag as motion"

2. AI which IS genuinely AI, but is not the kind of AI everyone is talking about right now

"AI", by which I mean machine learning using linear algebra, is very good at being fed a lot of training data, and then coming up with an ability to go and categorize real information.

The AI technology that looks at cells and determines whether they're cancer or not, that is using this technology. OCR (Optical Character Recognition) is the technology that can take an image of hand-written text and transcribe it. Again, it's using linear algebra, so yes it's AI.

Many other such examples exist, and have been around for quite a good number of years. They share the genre of technology, which is machine learning models, but these are not the Large Language Model Generative AI that is all over the media. Criticizing these would be like criticizing airplanes when you're actually mad at military drones. It's the same "makes fly in the air" technology but their impact is very different.

3. The AI we ARE talking about. "Chat-gpt" type of Generative AI which uses LLMs ("Large Language Models")

If there was one word I wish people would know in all this, it's LLM (Large Language Model). This describes the KIND of machine learning model that Chat-GPT/midjourney/stablediffusion are fueled by. They're so extremely powerfully trained on human language that they can take an input of conversational language and create a predictive output that is human coherent. (I am less certain what additional technology fuels art-creation, specifically, but considering the AI art generation has risen hand-in-hand with the advent of powerful LLM, I'm at least confident in saying it is still corely LLM).

This technology isn't exactly brand new (predictive text has been using it, but more like the mostly innocent and much less successful older sibling of some celebrity, who no one really thinks about.) But the scale and power of LLM-based AI technology is what is new with Chat-GPT.

This is the generative AI, and even better, the large language model generative AI.

(Data scientists, feel free to add on or correct anything.)

[reasonsforhope]

"As a Deaf man, Adam Munder has long been advocating for communication rights in a world that chiefly caters to hearing people. 

The Intel software engineer and his wife — who is also Deaf — are often unable to use American Sign Language in daily interactions, instead defaulting to texting on a smartphone or passing a pen and paper back and forth with service workers, teachers, and lawyers. 

It can make simple tasks, like ordering coffee, more complicated than it should be. 

But there are life events that hold greater weight than a cup of coffee. 

Recently, Munder and his wife took their daughter in for a doctor’s appointment — and no interpreter was available. 

To their surprise, their doctor said: “It’s alright, we’ll just have your daughter interpret for you!” ...

That day at the doctor’s office came at the heels of a thousand frustrating interactions and miscommunications — and Munder is not isolated in his experience.

“Where I live in Arizona, there are more than 1.1 million individuals with a hearing loss,” Munder said, “and only about 400 licensed interpreters.”

In addition to being hard to find, interpreters are expensive. And texting and writing aren’t always practical options — they leave out the emotion, detail, and nuance of a spoken conversation. 

ASL is a rich, complex language with its own grammar and culture; a subtle change in speed, direction, facial expression, or gesture can completely change the meaning and tone of a sign. 

“Writing back and forth on paper and pen or using a smartphone to text is not equivalent to American Sign Language,” Munder emphasized. “The details and nuance that make us human are lost in both our personal and business conversations.”

His solution? An AI-powered platform called Omnibridge. 

“My team has established this bridge between the Deaf world and the hearing world, bringing these worlds together without forcing one to adapt to the other,” Munder said. 

Trained on thousands of signs, Omnibridge is engineered to transcribe spoken English and interpret sign language on screen in seconds...

“Our dream is that the technology will be available to everyone, everywhere,” Munder said. “I feel like three to four years from now, we're going to have an app on a phone. Our team has already started working on a cloud-based product, and we're hoping that will be an easy switch from cloud to mobile to an app.” ...

At its heart, Omnibridge is a testament to the positive capabilities of artificial intelligence. "

-via GoodGoodGood, October 25, 2024. More info below the cut!

To test an alpha version of his invention, Munder welcomed TED associate Hasiba Haq on stage. 

“I want to show you how this could have changed my interaction at the doctor appointment, had this been available,” Munder said. 

He went on to explain that the software would generate a bi-directional conversation, in which Munder’s signs would appear as blue text and spoken word would appear in gray. 

At first, there was a brief hiccup on the TED stage. Haq, who was standing in as the doctor’s office receptionist, spoke — but the screen remained blank. 

“I don’t believe this; this is the first time that AI has ever failed,” Munder joked, getting a big laugh from the crowd. “Thanks for your patience.”

After a quick reboot, they rolled with the punches and tried again.

Haq asked: “Hi, how’s it going?” 

Her words popped up in blue. 

Munder signed in reply: “I am good.” 

His response popped up in gray. 

Back and forth, they recreated the scene from the doctor’s office. But this time Munder retained his autonomy, and no one suggested a 7-year-old should play interpreter. 

Munder’s TED debut and tech demonstration didn’t happen overnight — the engineer has been working on Omnibridge for over a decade. 

“It takes a lot to build something like this,” Munder told Good Good Good in an exclusive interview, communicating with our team in ASL. “It couldn't just be one or two people. It takes a large team, a lot of resources, millions and millions of dollars to work on a project like this.” 

After five years of pitching and research, Intel handpicked Munder’s team for a specialty training program. It was through that backing that Omnibridge began to truly take shape...

“Our dream is that the technology will be available to everyone, everywhere,” Munder said. “I feel like three to four years from now, we're going to have an app on a phone. Our team has already started working on a cloud-based product, and we're hoping that will be an easy switch from cloud to mobile to an app.” 

In order to achieve that dream — of transposing their technology to a smartphone — Munder and his team have to play a bit of a waiting game. Today, their platform necessitates building the technology on a PC, with an AI engine. 

“A lot of things don't have those AI PC types of chips,” Munder explained. “But as the technology evolves, we expect that smartphones will start to include AI engines. They'll start to include the capability in processing within smartphones. It will take time for the technology to catch up to it, and it probably won't need the power that we're requiring right now on a PC.” 

At its heart, Omnibridge is a testament to the positive capabilities of artificial intelligence. 

But it is more than a transcription service — it allows people to have face-to-face conversations with each other. There’s a world of difference between passing around a phone or pen and paper and looking someone in the eyes when you speak to them. 

It also allows Deaf people to speak ASL directly, without doing the mental gymnastics of translating their words into English.

“For me, English is my second language,” Munder told Good Good Good. “So when I write in English, I have to think: How am I going to adjust the words? How am I going to write it just right so somebody can understand me? It takes me some time and effort, and it's hard for me to express myself actually in doing that. This technology allows someone to be able to express themselves in their native language.” 

Ultimately, Munder said that Omnibridge is about “bringing humanity back” to these conversations. 

“We’re changing the world through the power of AI, not just revolutionizing technology, but enhancing that human connection,” Munder said at the end of his TED Talk. 

“It’s two languages,” he concluded, “signed and spoken, in one seamless conversation.”"

-via GoodGoodGood, October 25, 2024

[sunsmartglobal-blog]

Navigating the Challenges of Implementing Conversational AI in Banking

Integration with Legacy Systems:

Problem: Many banks operate on outdated legacy systems that are not easily compatible with new technologies. Solution: Developing robust APIs and middleware solutions to bridge the gap between legacy systems and modern Conversational AI platforms.

Data Privacy and Security:

Problem: Ensuring the privacy and security of sensitive customer data is paramount in banking. Solution: Implementing advanced encryption methods, strict access controls, and regular security audits to protect data integrity and comply with regulatory standards.

Accurate and Contextual Responses:

Problem: Conversational AI needs to understand and respond accurately to a wide range of customer queries. Solution: Utilizing machine learning algorithms and natural language processing (NLP) to continually improve the AI's understanding and contextual accuracy.

Customer Trust and Adoption:

Problem: Gaining customer trust in Conversational AI for handling financial matters. Solution: Offering transparent communication about how the AI works, ensuring high reliability, and providing options for human assistance when needed.

Scalability and Performance:

Problem: Handling a large volume of interactions without compromising performance. Solution: Implementing scalable cloud-based solutions and optimizing AI models to handle peak loads efficiently.

About Herbie.AI: Herbie.ai specializes in AI-driven solutions tailored for the banking industry. Our flagship product leverages advanced artificial intelligence to enhance customer experiences, streamline operations, and mitigate risks for financial institutions. With personalized customer engagement, fraud detection, process automation, risk management, and predictive analytics, we empower banks to thrive in the digital age and deliver exceptional value to their customers.

WhatsApp link: https://wa.link/g2d5fw Mail: sales@herbie.ai Website: www.herbie.ai | https://www.herbie.ai/industries/banking/

Follow us: Twitter - https://twitter.com/HerbieAI Instagram - https://www.instagram.com/herbie.ai/ LinkedIn - https://www.linkedin.com/company/herbieai/ Facebook - https://www.facebook.com/HerbieAI/

[queeranarchism]

It's interesting how the student protests are taking material action (occupying space with their bodies) to achieve a material goal (divestment)

and the focus of online discourse is not how we might best materially support their action or do stuff on our own, but whether the focus on the student protests 'distracts from what is happening in Gaza'.

& I'd say that no, I don't think it distracts from Gaza. For sure, there is a known pattern of focusing far more on any American suffering than on the suffering of thousands of Palestinians, but in this case I believe that the student protests have returned Gaza back to the center of the media's attention when it was eager to move on. There's noticeably more media content about Gaza itself now than 2 weeks ago. I also believe these protests have the potential to get some up-until-now-closed-minds to start questioning the status quo's narratives about Gaza and begin to educate themselves.

And very importantly, to those within the struggle, the protests and the violent police response make it abundantly clear how interlinked our liberations are. When cops trained in Israeli techniques brutalize protestors and intelligence software made in Israel tracks their phones and faces, it is undeniable that freeing Palestine and freeing ourselves are inseparable struggles.

But also, maybe our conversations about a material struggle shouldn't be primarily focused on which content gets reblogs. Like, that conversation isn't entirely unimportant. But it shouldn't, ya know, distract us from the material struggle.

[wolfliving]

Dead AI IoT

Humane AI finally shuts down, HP pays $116m for the pieces — not including the Ai Pin

By David Gerard on 19 February 2025

Humane, creator of the fabulous and literally nonfunctional Ai Pin gadget — that's "Ai," not "AI" — has finally thrown in the towel.

After Humane took $230 million in venture funding and tried and failed to sell itself for $1 billion, Hewlett-Packard is paying $116 million to acqui-hire most of the team and get Humane’s software and patents. [Humane, archive]

There was also some burbling from HP about “an intelligent ecosystem across all HP devices from AI PCs to smart printers and connected conference rooms,” which probably means floundering a bit then selling the software on once again, as they did with Palm and WebOS.

We’re sure that Humane’s venture capital backers — including Sam Altman, Microsoft, and Marc Benioff — will be delighted that minus-50% is now the expected realized return on AI investments in the bubble.

HP is notably not taking on the Ai Pin itself — probably because it’s completely useless and doesn’t work. The hardware overheats and fails, the projected display isn’t visible in sunlight, and the software chains together LLMs to fail to understand or translate conversations. Also, it might catch fire.

The remaining Ai Pins will work until February 28, when the back-end servers at Humane shut down. After that date, you can ... check the battery level? Pretty good for a $700 gadget with a $24/month subscription. We’re sure both customers will be delighted. ]Humane, archive; Humane, archive]