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Why Good Conversations Matter and How AI Can Help – ReadWrite



Why Good Conversations Matter and How AI Can Help - ReadWrite

Communication is the cornerstone of society. It’s how we build trust and lasting relationships personally, professionally, and politically. Good conversation is more than a perk of good company – it’s the lifeblood of every interaction we have.

Most major technological innovations in the last two hundred years have focused on how to keep people connected and provide a more instantaneous and, now, bespoke set of communication tools. We’ve been so focused on connecting more people as quickly as possible that we don’t often stop to measure the quality of the conversations held.

Why Good Conversations Matter and How AI Can Help

What makes a good conversation? How do you quantify something that is so important, but that for millennia has been a purely qualitative observation, almost entirely subjective to the people in that conversation?

Let’s take a closer look at the value of good conversation, how to measure it, and subsequently what needs to be done to improve on it over time.

How to Measure Conversational Value

Quality conversation is about the balance between two or more parties (Cornell University). It’s about finding the line between too much detail and over-simplification; the flow of information within a given subject area; who is asking questions and who is answering them.

The quality of a conversation may depend on its context.

When talking to a friend, quality might be defined by duration, the feeling of closeness you gain, and the outcome of the call. In business, a quality conversation is markedly different – often focused almost entirely on the results of the call.

Are there objective measures we can look to that help us measure and report on the objective quality of the conversation?

Five Key Elements of effective conversation.

Harvey Deutschendorf recently outlined five key elements to an effective conversation in Fast Company:

  1. Make it about the other person.
  2. Practice active listening.
  3. Move the conversation to a deeper level.
  4. Ask good questions.
  5. Consider time and space.

As you can see, each of these touches on the attention given to the other party in a conversation.

When you are in a conversation you can adjust your own contribution by asking yourself these questions.

Did you listen to them? Did you feel listened to? Is the conversation moving beyond high-level chitchat? Are strong questions being asked by both sides of the conversation? Did you adjust the speed and depth of the conversation to the time and space allotted for it?

Why are conversations difficult in this day and age?

One of the reasons so many people struggle with these questions in the 21st century is how truncated conversations have become. At any given time, you have access to 80% of the people you know.

You can call, text, instant message, or email someone from anywhere in the world at any time, so conversations are most often abbreviated. In many cases, your conversations don’t exist the way they did even 20 years ago.

When was the last time you called someone to ask a simple question?

As a result, many people have grown rusty. Conversations are harder to hold, and that affects both personal relationships and business interactions. Even professionals, customer service agents who answer calls 40 hours a week, struggle to get the conversations and parts of speech right.

AI is being developed to address the discrepancies and lag-time in conversations.

During the pandemic especially, the use of smart replies in email and text messaging systems has grown substantially. According to a recent study in Computers in Human Behavior, participants trusted AI systems and their smart reply functions more than the people with which they were communicating.

We are assuming that individuals were designating some of the responsibility in conversational problems to the AI.

The underlying issue with these systems, however, is that most of the design work that goes into these systems focuses on the user interface — not the resulting impact on the conversation.

The same can be said for almost all of the tools we have at our disposal to address conversations via technology.

AI Challenges in Good Conversational Improvements

In a recent paper presented at the 2019 NAACL conference, researchers presented the specific challenges faced in natural language generation tasks. Specifically, they outlined how less open-ended tasks like machine translation and sentence compression that provided mostly word-level decisions, and for which control is less important, were more accurate than open-ended conversational elements.

These open-ended elements, however, are where most human conversations develop and thrive. They include abstractive summarization, story generation, and, the most complex for machines to replicate, chitchat dialogue.

Why are open-ended more complex than the close-ended elements?

They require more high-level decisions and there is generally no “correct answer”. The result is that control is important and it’s vital that the machine is able to ask and answer questions like “What should we discuss next?” in a way that is conducive to continued quality conversation.

To evaluate chatbot quality for their experiment, the researchers identified six conversational aspects that humans can judge:

  • Does the bot avoid repeating itself?
  • Is the conversation interesting in general?
  • Does the bot make sense in the conversation?
  • How fluent is the bot in the colloquial language of the conversation?
  • Does the bot listen to what the user is saying and respond accordingly?
  • How inquisitive is the bot?

These align closely with the five elements of a good conversation outlined in the Fast Company article above. Effectively, how well can an AI system appear engaged and interested in what is being said to it?

Measure and respond to human emotions

A big part of addressing these challenges is to ensure AI systems have the ability to measure and respond to human emotion. Is the human subject interested? Engaged? Eager to learn more? Growing frustrated? These are all elements that can be measured in human voice with emotion AI, which can, in turn, make voice assistants, bots, and human conversations more engaging.

How to Improve Conversation

So if the technology is designed for the user without consideration for the other human being in a conversation, how do we improve conversation?

AI and ML tools can train customer service

Fortunately, there are AI and ML tools designed to do just this. AI suggestions are a part of life already. From Google’s search results to Siri and Alex’s automated suggestions on our smart devices, we’re being given recommendations on a near-daily basis for where to eat, when to leave for work, what to watch or listen to, and much more.

Healthcare usage

In conversation, the same concept can be immensely valuable to support and improve how we talk to one another. AI-mediated conversations are being used in healthcare to supplement how healthcare professionals talk to those with mental health issues, autism, and other communication impairments.

In palliative care, AI is being used to measure the key elements of both practitioner and patient and create the right match between them.

How do you respond to specific questions

By measuring things like how someone responds to certain types of questions, conversational style, cadence, and tone of voice, and matching them with someone who delivers the right combination of those things, the quality of conversations can be immensely improved.

This has applications not only in healthcare but in customer service centers where the right match between customer and agent can not only improve outcomes but expedite service.

Dating apps

It can be used in dating apps where the entire business model is based on being able to create compatible matches between two people. Even in casual conversational tools like instant messengers.

Call routing

Specifically, AI-Mediated Conversations (AI-MC) are being used to automate call routing to agents based on emotion AI and voice data. They evaluate and match customers to the correct customer service agent based on observed data.

Matching the customer to the customer service agent will help to improve outcomes in a conversation. Some ways include reducing costs by improving handling times and first-call resolution rates.

Mental health and performance

Supporting the mental health and performance of agents by reducing the number of challenging calls they must field is another way.

Measuring and Responding to Challenges in Conversations

The quality of good conversation is increasingly something we can quantify to improve connections in business, personal relationships, and healthcare.

With miscommunications and inexact matches accounting for billions of dollars in lost productivity each year, the ability to evaluate and respond to personality data through behavioral analytics and voice data is a huge leap forward for the quality of conversations.

Image Credit: bigstock; merry entrepreneur; thank you!

Rana Gujral

Rana Gujral is an entrepreneur, speaker, investor and the CEO of Behavioral Signals, an enterprise software company that delivers a robust and fast evolving emotion AI engine that introduces emotional intelligence into speech recognition technology. Rana has been awarded the ‘Entrepreneur of the Month’ by CIO Magazine and the ‘US China Pioneer’ Award by IEIE, he has been listed among Top 10 Entrepreneurs to follow in 2017 by Huffington Post.


Fintech Kennek raises $12.5M seed round to digitize lending



Google eyed for $2 billion Anthropic deal after major Amazon play

London-based fintech startup Kennek has raised $12.5 million in seed funding to expand its lending operating system.

According to an Oct. 10 report, the round was led by HV Capital and included participation from Dutch Founders Fund, AlbionVC, FFVC, Plug & Play Ventures, and Syndicate One. Kennek offers software-as-a-service tools to help non-bank lenders streamline their operations using open banking, open finance, and payments.

The platform aims to automate time-consuming manual tasks and consolidate fragmented data to simplify lending. Xavier De Pauw, founder of Kennek said:

“Until kennek, lenders had to devote countless hours to menial operational tasks and deal with jumbled and hard-coded data – which makes every other part of lending a headache. As former lenders ourselves, we lived and breathed these frustrations, and built kennek to make them a thing of the past.”

The company said the latest funding round was oversubscribed and closed quickly despite the challenging fundraising environment. The new capital will be used to expand Kennek’s engineering team and strengthen its market position in the UK while exploring expansion into other European markets. Barbod Namini, Partner at lead investor HV Capital, commented on the investment:

“Kennek has developed an ambitious and genuinely unique proposition which we think can be the foundation of the entire alternative lending space. […] It is a complicated market and a solution that brings together all information and stakeholders onto a single platform is highly compelling for both lenders & the ecosystem as a whole.”

The fintech lending space has grown rapidly in recent years, but many lenders still rely on legacy systems and manual processes that limit efficiency and scalability. Kennek aims to leverage open banking and data integration to provide lenders with a more streamlined, automated lending experience.

The seed funding will allow the London-based startup to continue developing its platform and expanding its team to meet demand from non-bank lenders looking to digitize operations. Kennek’s focus on the UK and Europe also comes amid rising adoption of open banking and open finance in the regions.

Featured Image Credit: Photo from; Thank you!

Radek Zielinski

Radek Zielinski is an experienced technology and financial journalist with a passion for cybersecurity and futurology.

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Fortune 500’s race for generative AI breakthroughs



Deanna Ritchie

As excitement around generative AI grows, Fortune 500 companies, including Goldman Sachs, are carefully examining the possible applications of this technology. A recent survey of U.S. executives indicated that 60% believe generative AI will substantially impact their businesses in the long term. However, they anticipate a one to two-year timeframe before implementing their initial solutions. This optimism stems from the potential of generative AI to revolutionize various aspects of businesses, from enhancing customer experiences to optimizing internal processes. In the short term, companies will likely focus on pilot projects and experimentation, gradually integrating generative AI into their operations as they witness its positive influence on efficiency and profitability.

Goldman Sachs’ Cautious Approach to Implementing Generative AI

In a recent interview, Goldman Sachs CIO Marco Argenti revealed that the firm has not yet implemented any generative AI use cases. Instead, the company focuses on experimentation and setting high standards before adopting the technology. Argenti recognized the desire for outcomes in areas like developer and operational efficiency but emphasized ensuring precision before putting experimental AI use cases into production.

According to Argenti, striking the right balance between driving innovation and maintaining accuracy is crucial for successfully integrating generative AI within the firm. Goldman Sachs intends to continue exploring this emerging technology’s potential benefits and applications while diligently assessing risks to ensure it meets the company’s stringent quality standards.

One possible application for Goldman Sachs is in software development, where the company has observed a 20-40% productivity increase during its trials. The goal is for 1,000 developers to utilize generative AI tools by year’s end. However, Argenti emphasized that a well-defined expectation of return on investment is necessary before fully integrating generative AI into production.

To achieve this, the company plans to implement a systematic and strategic approach to adopting generative AI, ensuring that it complements and enhances the skills of its developers. Additionally, Goldman Sachs intends to evaluate the long-term impact of generative AI on their software development processes and the overall quality of the applications being developed.

Goldman Sachs’ approach to AI implementation goes beyond merely executing models. The firm has created a platform encompassing technical, legal, and compliance assessments to filter out improper content and keep track of all interactions. This comprehensive system ensures seamless integration of artificial intelligence in operations while adhering to regulatory standards and maintaining client confidentiality. Moreover, the platform continuously improves and adapts its algorithms, allowing Goldman Sachs to stay at the forefront of technology and offer its clients the most efficient and secure services.

Featured Image Credit: Photo by Google DeepMind; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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UK seizes web3 opportunity simplifying crypto regulations



Deanna Ritchie

As Web3 companies increasingly consider leaving the United States due to regulatory ambiguity, the United Kingdom must simplify its cryptocurrency regulations to attract these businesses. The conservative think tank Policy Exchange recently released a report detailing ten suggestions for improving Web3 regulation in the country. Among the recommendations are reducing liability for token holders in decentralized autonomous organizations (DAOs) and encouraging the Financial Conduct Authority (FCA) to adopt alternative Know Your Customer (KYC) methodologies, such as digital identities and blockchain analytics tools. These suggestions aim to position the UK as a hub for Web3 innovation and attract blockchain-based businesses looking for a more conducive regulatory environment.

Streamlining Cryptocurrency Regulations for Innovation

To make it easier for emerging Web3 companies to navigate existing legal frameworks and contribute to the UK’s digital economy growth, the government must streamline cryptocurrency regulations and adopt forward-looking approaches. By making the regulatory landscape clear and straightforward, the UK can create an environment that fosters innovation, growth, and competitiveness in the global fintech industry.

The Policy Exchange report also recommends not weakening self-hosted wallets or treating proof-of-stake (PoS) services as financial services. This approach aims to protect the fundamental principles of decentralization and user autonomy while strongly emphasizing security and regulatory compliance. By doing so, the UK can nurture an environment that encourages innovation and the continued growth of blockchain technology.

Despite recent strict measures by UK authorities, such as His Majesty’s Treasury and the FCA, toward the digital assets sector, the proposed changes in the Policy Exchange report strive to make the UK a more attractive location for Web3 enterprises. By adopting these suggestions, the UK can demonstrate its commitment to fostering innovation in the rapidly evolving blockchain and cryptocurrency industries while ensuring a robust and transparent regulatory environment.

The ongoing uncertainty surrounding cryptocurrency regulations in various countries has prompted Web3 companies to explore alternative jurisdictions with more precise legal frameworks. As the United States grapples with regulatory ambiguity, the United Kingdom can position itself as a hub for Web3 innovation by simplifying and streamlining its cryptocurrency regulations.

Featured Image Credit: Photo by Jonathan Borba; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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