Digital transformation (DX) has recently become a pressing issue for many companies as the latest digital technologies, such as artificial intelligence and the Internet of Things, can be easily utilized. However, devising new business models is not easy for compa-nies, though they can improve their operations through digital technologies. Thus, business model design support methods are needed by people who lack digital tech-nology expertise. In contrast, large language models (LLMs) represented by ChatGPT and natural language processing utilizing LLMs have been developed revolutionarily. A business model design support system that utilizes these technologies has great potential. However, research on this area is scant. Accordingly, this study proposes an LLM-based method for comparing and analyzing similar companies from different business do-mains as a first step toward business model design support utilizing LLMs. This method can support idea generation in digital business model design.

Deepfake audio poses a rising threat in communication platforms, necessitating real-time detection for audio stream integrity. Unlike traditional non-real-time approaches, this study assesses the viability of employing static deepfake audio detection models in real-time communication platforms. An executable software is developed for cross-platform compatibility, enabling real-time execution. Two deepfake audio detection models based on Resnet and LCNN architectures are implemented using the ASVspoof 2019 dataset, achieving benchmark performances compared to ASVspoof 2019 challenge baselines. The study proposes strategies and frameworks for enhancing these models, paving the way for real-time deepfake audio detection in communication platforms. This work contributes to the advancement of audio stream security, ensuring robust detection capabilities in dynamic, real-time communication scenarios.

Staying on top of tech trends is always important for business, but it's also a challenge when trends are changing so rapidly. Few aspects of the business world have changed as dramatically as communications. Whether you serve the general public or offer B2B services, you need to build a robust communication infrastructure to meet your customers' needs. Check out the following eight signs that your communication platform may need an upgrade. Related Post: Why Hiring a Communications Agency Can be Helpful? If you notice that your customers seem to be frustrated whenever they engage with you, there's a good chance that your communication platform is exacerbating the problem.

While multi-robot systems have been broadly researched and deployed, their success is built chiefly upon the dependency on network infrastructures, whether wired or wireless. Aiming at the first steps toward de-coupling the application of multi-robot systems from the reliance on network infrastructures, this paper proposes a human-friendly verbal communication platform for multi-robot systems, following the deliberately designed principles of being adaptable, transparent, and secure. The platform is network independent and is subsequently capable of functioning in network infrastructure lacking environments from underwater to planet explorations. A series of experiments were conducted to demonstrate the platform's capability in multi-robot systems communication and task coordination, showing its potential in infrastructure-free applications. To benefit the community, we have made the codes open source at https://github.com/jynxmagic/MSc_AI_project

Marchex, an artificial intelligence-powered conversation intelligence company, has integrated with platforms from leading communication platform-as-a-service (CPaaS), unified communication-as-a-service (UCaaS), and contact center-as-a-service (CCaaS) providers. These integrations are available via Marchex Anywhere, a new integrations and applications hub. Using Marchex's AI-powered conversation intelligence services, sales; customer engagement; and marketing teams can do the following: "Marchex Anywhere brings our market-leading conversation intelligence services to a rapidly expanding universe of businesses looking to enhance their existing sales, customer engagement, marketing and communication platforms with the power of actionable insights from customer conversations," said Ryan Polley, Marchex's chief operating officer, in a statement. "This reflects Marchex's continued momentum in expanding our conversation intelligence technology and data in innovative and practical ways to deliver the outcomes that customers want." Marchex Zero Code Integrations provide direct data integrations with sales, customer engagement, and marketing platforms, such as Salesforce Sales Cloud, chat systems, Google Marketing Platform, and Adobe Experience Platform.

Remitter, a fintech platform that improves bill payment rates by communicating to consumers digitally, has just finalised its acquisition of US-based Mercantile Adjustment Bureau, following a successful pre-IPO USD $12m cap raise led by Canaccord Genuity. The raise was cornerstoned by Allium Capital and Casey Capital. Remitter is a white-label communications platform, founded in Australia, which uses AI to optimise customer engagement and enhance the recovery of accounts receivables. Currently many organisations face challenges in collecting bill payments on time, with 46% of customers paying late according to Aite Group research. Remitter entered the US market in 2020, following two years of development including compliance across states and territories and collaboration with clients to ensure an optimal feature set. "Entering the US market involved ensuring the Remitter platform was compliant in all 52 states, each with its own different laws and regulations around payments.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. In March, the FBI released a report declaring that malicious actors almost certainly will leverage "synthetic content" for cyber and foreign influence operations in the next 12-18 months. This synthetic content includes deepfakes, audio or video that is either wholly created or altered by artificial intelligence or machine learning to convincingly misrepresent someone as doing or saying something that was not actually done or said. We've all heard the story about the CEO whose voice was imitated convincingly enough to initiate a wire transfer of $243,000. Now, the constant Zoom meetings of the anywhere workforce era have created a wealth of audio and video data that can be fed into a machine learning system to create a compelling duplicate.

Brian is the co-founder and CTO of Dialpad, the leader in AI-powered communications and collaboration. "Unified communications" is a term that has been used for the past 30 years to describe anything that brings together some combination of voice, video and messaging. As technology progresses, more platforms have declared themselves to be "unified." Unfortunately, most simply give the illusion of unification. The rise of the cloud supported more seamless product integrations -- which, in turn, made it easier to declare something "unified" as long as it strung together a messaging and calling component into one interface.

The tech sphere is showering money recently. For the past two decades, artificial intelligence was encountering significant growth across many domains. But thanks to the Covid-19 pandemic, the adoption was further accelerated. The sudden surge in disruptive technologies' usage has eventually opened the door for investments in artificial intelligence. Investors are also looking to back AI companies that will one day flourish like Apple, Google, Netflix, Amazon, etc. Artificial intelligence is an umbrella term that covers many topics including machine learning, data analytics, data science, natural language processing, etc.

Transcription service companies continue to make claims about their AI, but in the absence of any formal set of benchmarks for natural language processing (NLP), most of those claims lack the context required to make a precise apples-to-apples comparison. Dialpad, for example, recently announced that its Voice Intelligence AI technology has surpassed rivals in terms of both keyword and general accuracy. Along with Google, IBM, Microsoft, Cisco, Chorus.ai, Avaya, Zoom, and Amazon Web Services (AWS), Dialpad provides end users with automated speech recognition (ASR) and NLP capabilities that can be applied to voice calls and videoconferencing sessions in real time. An analysis of general word and keyword accuracy Dialpad published claims it achieves a word accuracy rating of 82.3% and a keyword accuracy rating of 92.5%.