Mediaboss Marketing

History shows civil wars to be among the messiest, most horrifying of human affairs. So Princeton professor Arvind Narayanan and his PhD student Sayash Kapoor got suspicious last year when they discovered a strand of political science research claiming to predict when a civil war will break out with more than 90 percent accuracy, thanks to artificial intelligence Yet when the Princeton researchers looked more closely, many of the results turned out to be a mirage.

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They were claiming near-perfect accuracy, but we found that in each of these cases, there was an error in the machine-learning pipeline, says Kapoor. When he and Narayanan fixed those errors, in every instance they found that modern AI offered virtually no advantage.

That experience prompted the Princeton pair to investigate whether misapplication of machine learning was distorting results in other fieldsand to conclude that incorrect use of the technique is a widespread problem in modern science.

The idea that you can take a four-hour-long online course and then use machine learning in your scientific research has become so overblown, Kapoor says. People have not stopped to think about where things can potentially go wrong.

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Conclusions drawn by many artificial intelligence studies cannot be replicated. Here's why this is a concern - Genetic Literacy Project

DUBLIN--(BUSINESS WIRE)--The "Conversational Artificial Intelligence (AI) and Voice Cloning Market: Next Generation Enterprise Solutions by Use Case, Application, and Industry Verticals 2020 - 2027" report has been added to ResearchAndMarkets.com's offering.

This report evaluates the market drivers and uses cases for conversational AI and voice cloning solutions to execute various business functions such as CRM. The report analyzes the core technologies used to build conversational AI and voice cloning solutions along with the potential application areas across industry verticals.

The report provides an analysis of leading company strategies, capabilities, and offerings. Forecasts include technologies, solutions, services, applications, tools, and platforms from 2022 to 2027. It also provides forecasts by deployment type, business type (enterprise, SMB, government), industry vertical, and specific applications.

Select Report Findings:

Traditional peer-to-peer communication systems consisting of emails, phone calls, text messages, and face to face meetings have hugely been disrupted with the widespread adoption of next-generation platforms such as social media, messaging apps, and voice-based assistants.

This has triggered a major paradigm shift in customer behavior to prefer these alternative communications platforms, providing omnichannel experience regardless of devices. Not surprisingly, younger people are at the tip of the spear of the adoption curve for text but also voice, video, and image sharing.

For additional market segments, a shift occurs in terms of customers' business engagement expectations when they realize they may engage over their favorite chat platform using text, voice, and video communications. Conversational AI plays a profound role here, automatically communicating with customers as if a real human being, but in actuality an authentically human-sounding, AI-powered bot.

Conversational AI leverages natural language, machine learning, and other technologies to help omnichannel engagement platforms better understand and interact with customers, providing automated and personalized experiences across any channel including web, applications, mobile, and other platforms. Businesses can leverage opportunities to automate customer service operations as well as marketing and sales initiatives.

Businesses are beginning to integrate conversational AI through voice assistants, chatbots, and messaging apps. We expect that 36% of enterprises will shift their customer support function entirely to virtual assistants by 2027. This prediction is supported by our findings that indicate most customers prefer to shop with business through chat applications. This represents a massive shift from five years ago.

Whereas conversational AI merely sounds like an actual human, voice cloning mimics a known person's voice that is distinguishable as someone that a person would believe is the real person that they know. Like basic conversational AI, it may be used with various applications and industry verticals, particularly retail and other consumer services-oriented business areas.

With voice cloning, businesses can introduce a customer familiar voice to build a long-term relationship and ensure a better customer experience. Voice cloning models are trained through some data set, typically within only a few hours of recorded speech. It also leverages AI and machine learning technologies to train models so that it may engage in natural-sounding, real-time conversations with customers.

In addition to shifting customer behaviors and expectations, there are some other factors that drive enterprise and contact service providers towards leveraging conversational AI and voice cloning solutions. Some of the factors include saving time for customer service, improving real-time accessibility, increasing efficiency, reducing customer acquisition costs, building long-term relationships, handling customer queries effectively, and reducing customer complaints.

Pandemic mitigation is expected to add a significant growth factor to the conversational AI and voice cloning market as businesses seek to automate operations and enhance worker safety as well as support governmental rules and regulations. As social distancing, remote work and operation, and massive digitization continue to grow, businesses will be more reliant on providing remote services to customers.

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction

2.1 Conversational AI

2.1.1 What is Conversational AI

2.1.2 Conversational AI Architecture

2.1.3 Core Challenges

2.1.4 Core Principles

2.1.5 Technology Component

2.1.6 Conversational AI and Chatbot

2.1.7 Automatic Speech Recognition

2.1.8 Growth Drivers

2.2 Voice Cloning

2.2.1 What is Voice Cloning

2.2.2 Voice Cloning Architecture

2.2.3 AI Voice Cloning

2.2.4 Voice Anti-Spoofing and Fraud Detection

2.2.5 Core Challenges

2.2.6 Growth Drivers

2.3 Building Conversational AI and Voice Cloning Solutions

2.4 AI-Enabled Personalization

2.5 Enterprise and Customer Benefits

2.6 Artificial General Intelligence

2.7 Artificial Super Intelligence

2.8 Market Drivers and Challenges

2.9 Value Chain

2.9.1 AI Companies

2.9.2 Software/Platform Companies

2.9.3 Analytics Providers

2.9.4 IoT Companies

2.9.5 Connectivity Providers

2.9.6 Enterprises and End Users

2.10 Regulatory Implications

2.11 Pandemic Impact

3.0 Technology and Application Analysis

3.1 Conversational AI and Voice Cloning Technology

3.1.1 Machine Learning and Deep Learning

3.1.2 Natural Language Processing

3.1.3 Automatic Speech Recognition

3.1.4 Computer Vision

3.2 Conversational AI and Voice Cloning Application

3.2.1 Chatbots

3.2.2 Intelligent Voice Assistants (IVA) System

3.2.3 Accessibility/ Messaging Application

3.2.4 Digital Games

3.2.5 Interactive Learning Application

3.3 Conversational AI and Voice Cloning Functions

3.3.1 Customer Support

3.3.2 Personal Assistant

3.3.3 Branding and Advertising

3.3.4 Customer Engagement and Retention

3.3.5 Employee Engagement and Onboarding

3.3.6 Data Privacy and Compliance

3.3.7 Campaign Analysis and Data Aggregation

3.4 Conversational AI and Voice Cloning Use Cases

3.4.1 Healthcare and Life Science

3.4.2 Education

3.4.3 Telecom, IT, and Internet

3.4.4 Bank and Financial Institution

3.4.5 Travel and Hospitality/Tourism

3.4.6 Media and Entertainment

3.4.7 Energy and Utilities

3.4.8 Government and Defense

3.4.9 Retail and E-commerce

3.4.10 Manufacturing

3.4.11 Automotive

3.5 Cloud Deployment and Enterprise AI Adoption

3.6 Software Platform and Tools

3.7 5G Deployment and Edge Computing

3.8 Smart Workplace and Service Automation

3.9 Public Safety and Governments

3.10 Ethical Implications

3.11 Social Scam, Theft, and Call Fraud

3.12 Augmented Reality and RCS Messaging

3.13 Multilingualism

3.14 M2M Communications

4.0 Company Analysis

4.1 Acapela Group

4.2 Alt Inc.

4.3 Amazon

4.4 Aristech GmbH

4.5 Artificial Solutions

4.6 AT&T

4.7 Avaamo

4.8 AmplifyReach

4.9 Baidu

4.10 CandyVoice

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$13.2 Billion Conversational Artificial Intelligence (AI) and Voice Cloning Market, 2027: Next Generation Enterprise Solutions by Use Case,...

New Jersey, N.J., Aug 22, 2022 Computer vision is a field of AI that trains computers to capture and interpret information from image and video data. By applying machine learning (ML) models to images, computers can classify objects and react, such as unlocking your smartphone when it recognizes your face.

The global AI in computer vision market size is expected to witness significant growth over the forecast period. Factors, such as rising demand for computer vision systems in automotive applications, growing demand for emotional AI, and high demand for quality inspection and automation, are driving the growth of the AI market in computer vision.

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Competitive landscape:

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Some of the Top companies Influencing this Market include:Facebook, Cognex, Avigilon, Basler AG, COGNEX Corporation, Qualcomm Technologies, Inc., Allied Vision Technologies GmbH, Apple Inc., Xilinx, Intel Corporation, Teledyne Technologies, Microsoft Corporation, Google LLC, NVIDIA Corporation

Market Scenario:

Firstly, this Computer Vision in Artificial Intelligence (AI) research report introduces the market by providing an overview which includes definition, applications, product launches, developments, challenges, and regions. The market is forecasted to reveal strong development by driven consumption in various markets. An analysis of the current market designs and other basic characteristics is provided in the Computer Vision in Artificial Intelligence (AI) report.

Regional Coverage:

The region-wise coverage of the market is mentioned in the report, mainly focusing on the regions:

Segmentation Analysis of the market

The market is segmented on the basis of the type, product, end users, raw materials, etc. the segmentation helps to deliver a precise explanation of the market

Market Segmentation: By Type

Hardware, Software

Market Segmentation: By Application

Image Recognition, Machine Learning, Other Applications

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An assessment of the market attractiveness with regard to the competition that new players and products are likely to present to older ones has been provided in the publication. The research report also mentions the innovations, new developments, marketing strategies, branding techniques, and products of the key participants present in the global Computer Vision in Artificial Intelligence (AI) market. To present a clear vision of the market the competitive landscape has been thoroughly analyzed utilizing the value chain analysis. The opportunities and threats present in the future for the key market players have also been emphasized in the publication.

This report aims to provide:

Table of Contents

Global Computer Vision in Artificial Intelligence (AI) Market Research Report 2022 2029

Chapter 1 Computer Vision in Artificial Intelligence (AI) Market Overview

Chapter 2 Global Economic Impact on Industry

Chapter 3 Global Market Competition by Manufacturers

Chapter 4 Global Production, Revenue (Value) by Region

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Market Analysis by Application

Chapter 8 Manufacturing Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Market Effect Factors Analysis

Chapter 12 Global Computer Vision in Artificial Intelligence (AI) Market Forecast

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[emailprotected]

+1 775 237 4157

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Computer Vision in Artificial Intelligence (AI) Market is Expected to Record the Massive Growth, with Prominent Key Players Facebook, Cognex, Avigilon...

Security and intelligence experts have advised President Muhammadu Buharis regime to use artificial intelligence (AI) to fight Boko Haram, bandits and other criminals terrorising Nigeria.

The experts spoke at the 15th International Security Conference and Award (ISCA), organised by the International Institute of Professional Security (IIPS), on Saturday in Abuja.

Director General of IIPS, Tony Ofoyetan, said deploying drones would strengthen the fight against insecurity.

There is need for more of technology in intelligence gathering and operational executions and the like. They need to understand the essence of inanimate intelligence, and that is actually the reason why we put on this conference, he said.

Mr Ofoyetan also advised the regime to look at the security challenges beyond the perspective of military actions. He added that the government should interrogate the possibility of international sponsorship of the various security challenges bedevilling the country.

Another security and intelligence expert, Kabiru Adamu, said, What this conference has done is to bring forward solutions using technology in managing insecurity in Nigeria. The reality is that technology is a force multiplier in a situation where you have paucity of funds, where you dont have enough personnel, then technology is the natural fallback.

He added, That is what this conference is proposing. All the papers that were presented discussed solutions around the use of technology, particularly the use of AI. Looking at the EndSARS crisis when Nigeria was caught unawares, its obvious our security agencies were not prepared for the kind of modernisation that took place in cyberspace.

The security expert added that Nigeria had no choice but to adopt technology in intelligence gathering to tackle the security challenges effectively.

Mr Adamu also called for the engagement of young people, especially experts in different aspects of cybersecurity, to support the military forces.

(NAN)

More here:
Buhari regime advised to use artificial intelligence to fight bandits, Boko Haram - Peoples Gazette

Meta, Wikipedia

Despite the efforts of over 30 million editors, Wikipedia sure aint perfect. Some information on Wikipedia lacks a genuine source or citationas we learned with the Pringle Man hoax, this can have a wide-ranging impact on culture or facts. But Meta, formerly Facebook, hopes to solve Wikipedias big problem with AI.

As detailed in a blog post and research paper, the Meta AI team created a dataset of over 134 million web pages to build a citation-checker AIcalled SIDE. Using natural language technology, SIDE can analyze a Wikipedia citation and determine whether its appropriate. It can also find new sources for information already published on Wikipedia.

Meta AI highlights the Blackfoot ConfederacyWikipedia article as an example of how SIDE can improve citations. If you scroll to the bottom of this article, youll learn that Joe Hipp was the first Native American to competefor the WBA World Heavyweight Titlea cool fact that is 100% true. But heres the problem; whoever wrote this factoid cited a source that has nothing to do with Joe Hipp or the Blackfeet Tribe.

In this case, Wikipedia editors failed to check the veracity of a citation (the problem has since been fixed). But if the editors had SIDE, they could have caught the bad citation early. And they wouldnt need to look for a new citation, as SIDE would automatically suggest one.

At least, this is the hypothesis put forth by Meta AI researchers. While SIDE is certainly an interesting tool, we still cant trust AI to understand language, context, or the veracity of anything published online. (To be fair, Meta AIs research paperdescribes SIDE as more of a demonstration than a working tool.)

Wikipedia editors can now test SIDE and assess its usefulness. The project is also available on Github. For what its worth, SIDE looks like a super-powered version of the tools that Wikipedia editors already use to improve their workflow. Its easy to see how such a tool could flag citations for humans to review, at the very least.

Source: Meta AI

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Meta Wants to Fixs Wikipedia Biggest Problem Using AI - Review Geek