Widespread frustration with rigid touch-tone Interactive Voice Response (IVR) systems for customer service underscores the need for more direct and intuitive language interaction. While speech technologies are necessary, the key challenge lies in routing intents from user phrasings to IVR menu paths, a task where Large Language Models (LLMs) show strong potential. Progress, however, is limited by data scarcity, as real IVR structures and interactions are often proprietary. We present a novel LLM-based methodology to address this gap. Using three distinct models, we synthesized a realistic 23-node IVR structure, generated 920 user intents (230 base and 690 augmented), and performed the routing task. We evaluate two prompt designs: descriptive hierarchical menus and flattened path representations, across both base and augmented datasets. Results show that flattened paths consistently yield higher accuracy, reaching 89.13% on the base dataset compared to 81.30% with the descriptive format, while augmentation introduces linguistic noise that slightly reduces performance. Confusion matrix analysis further suggests that low-performing routes may reflect not only model limitations but also redundancies in menu design. Overall, our findings demonstrate proof-of-concept that LLMs can enable IVR routing through a smoother, more seamless user experience -- moving customer service one step ahead of touch-tone menus.

Seven Bank has introduced a service allowing its account holders to deposit and withdraw cash using facial recognition technology at 26,000 automated teller machines across the country. Named Face Cash, the new service introduced Thursday enables depositors with Seven Bank or Shizuoka Bank to make deposits and withdrawals at Seven Bank's ATMs without cash cards or smartphones. Using NEC's facial recognition technology, depositors can move their money just by entering a password and a code number, after registering facial information on their first use. The new service, protected with security measures jointly developed by Seven Bank and NEC, will reduce risks including from unauthorized use committed through identity theft. Seven Bank aims to spread the use of Face Cash to accounts at other banks.

Providing a personalized user experience on information dense webpages helps users in reaching their end-goals sooner. We explore an automated approach to identifying user personas by leveraging high dimensional trajectory information from user sessions on webpages. While neural collaborative filtering (NCF) approaches pay little attention to token semantics, our method introduces SessionBERT, a Transformer-backed language model trained from scratch on the masked language modeling (mlm) objective for user trajectories (pages, metadata, billing in a session) aiming to capture semantics within them. Our results show that representations learned through SessionBERT are able to consistently outperform a BERT-base model providing a 3% and 1% relative improvement in F1-score for predicting page links and next services. We leverage SessionBERT and extend it to provide recommendations (top-5) for the next most-relevant services that a user would be likely to use. We achieve a HIT@5 of 58% from our recommendation model.

As businesses grapple with growing volumes of data collected and generated by a myriad of cloud-based applications, Amazon Web Services (AWS) unveiled a wide range of new applications and product enhancements this week at its annual re:Invent conference that are geared to optimize data analytics and governance, and bolster the computing infrastructure to do so. Over the last few days, the company launched new services and features across its storage, compute, analytics, machine learning, databases, and security services, and made its first foray into supply chain management. Here is a roundup of the major announcements, with links to articles containing more details about the updates. A major theme at re:Invent 2022 was Amazon's efforts to ease data management and analytics for enterprises, as the company announced a dozen updates to data services. The updates included the launch of two new capabilities--Amazon Aurora zero-ETL integration with Amazon Redshift and Amazon Redshift integration for Apache Spark--that it claims will make the extract, transform, load (ETL) process obsolete.

The automotive sector is undergoing dramatic changes. On several levels, global change, technological breakthroughs, and changing customer behaviour are all having an impact on the automobile business at the same time. Vehicle design, manufacturing, sales, maintenance, insurance, and financing are al ripe for revolutionary changes. Audi, Tesla, Hyundai, Benz, Nissan, and Kia are all working hard to include AI and automate their cars. Over the next decade, technological improvements and customer expectations will be the main drivers of change.

If you want to begin using machine learning in your applications, Microsoft offers several different ways to jumpstart development. One key technology, Microsoft's Azure Cognitive Services, offers a set of managed machine learning services with pretrained models and REST API endpoints. These models offer most of the common use cases, from working with text and language, to recognizing speech and images. Machine learning is still evolving, with new models being released and new hardware to help speed up inferencing, and so Microsoft regularly updates its Cognitive Services. The latest major update, announced at Build 2022, features a lot of changes to its tools for working with text, bringing three different services under one umbrella.

AWS has introduced SageMaker Studio Lab, a free service to help developers learn machine-learning techniques and experiment with the technology. SageMaker Studio Lab provides users with all of the basics to get started, including a JupyterLab IDE, model training on CPUs and GPUs and 15 GB of persistent storage. SageMaker Studio Lab has all the basics to create data analytics, scientific computing, and machine-learning projects with notebooks, which can be easily imported and exported via the Git repo or a private Amazon S3 bucket. SageMaker Studio Lab becomes an alternative to the popular Google Colab environment, providing free CPU/GPU access. Another enhancement for AWS SageMaker is a visual, no-code tool called SageMaker Canvas.

Hit play on the player above to hear the podcast and follow along with the transcript below.This transcript was automatically generated, and then edited for clarity in its current form. There may be some differences between the audio and the text. Welcome back to Talking Tech. If you used Amazon's Alexa, I will only be saying this name once by the way, because I don't want to trigger anyone who has an Echo speaker, but if you've used Amazon's digital voice assistant, obviously it's got many different ways it can help you play a song, set a reminder, set an alarm. Now Amazon's digital assistant has a new role, helping seniors and caregivers.

SAN FRANCISCO (REUTERS) - In September last year, Google's cloud unit looked into using artificial intelligence (AI) to help a financial firm decide whom to lend money to. It turned down the client's idea after weeks of internal discussions, deeming the project too ethically dicey because the AI technology could perpetuate biases like those around race and gender. Since early last year, Google has also blocked new AI features analysing emotions, fearing cultural insensitivity, while Microsoft restricted software mimicking voices and IBM rejected a client request for an advanced facial-recognition system. All these technologies were curbed by panels of executives or other leaders, according to interviews with AI ethics chiefs at the three US technology giants. Reported here for the first time, their vetoes and the deliberations that led to them reflect a nascent industry-wide drive to balance the pursuit of lucrative AI systems with a greater consideration of social responsibility.

In September last year, Google's cloud unit looked into using artificial intelligence to help a financial firm decide whom to lend money to. It turned down the client's idea after weeks of internal discussions, deeming the project too ethically dicey because the AI technology could perpetuate biases like those around race and gender. Since early last year, Google has also blocked new AI features analyzing emotions, fearing cultural insensitivity, while Microsoft restricted software mimicking voices and IBM rejected a client request for an advanced facial-recognition system. All these technologies were curbed by panels of executives or other leaders, according to interviews with AI ethics chiefs at the three U.S. technology giants. Reported here for the first time, their vetoes and the deliberations that led to them reflect a nascent industry-wide drive to balance the pursuit of lucrative AI systems with a greater consideration of social responsibility.