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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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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)

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

In his superb book, Dominion, historian Tom Holland finds parallels between the early Christians and todays judgemental theorists of gender and race. Both can be called social-justice warriors, he notes. Each sees a judgement day close at hand, and each has zealots who relish their role as judge, jury and hangman. Wokeness is just one modern mania that has a distinctly religious quality. Arguably, there are two other modern religions that eclipse wokeness in their scope and ambition: environmentalism and artificial intelligence (AI).

Environmentalism expresses a desire to subordinate human development and welfare to a new, all-encompassing mission that of reducing the atmospheric concentration of carbon dioxide. An emergency or a crisis has been declared by activists, one which supposedly requires the suspension of political and moral norms. Every aspect of our lives is recast into this new moral framework.

Karl Marx recognised how religion gives society its shape and moral order. He called religion the general theory of this world, its encyclopaedic compendium, its logic in popular form, its spiritual point dhonneur, its enthusiasm, its moral sanction, its solemn complement, and its universal basis of consolation and justification. But Marx also recognised religions devotion to the idea that human beings are exceptional and unique: It is the fantastic realisation of the human essence. Religion is a form of fetishised or estranged humanism, Marx was saying.

Environmentalism turns this celebration of humanity on its head. Human activities are measured by the harm or impacts they cause to the natural order, and all human activity is therefore sinful. We ate the forbidden fruit by burning fossil fuels and by daring to increase human welfare and now we must pay. Even the UK prime minister signals his support for this philosophical belief when he describes the Industrial Revolution as a derangement of nature, or a doomsday machine.

Equally religious, and equally anti-human, is the current infatuation with AI. We are currently in the third wave of enthusiasm for AI in 65 years, during which periods of high hopes and investment in AI have been followed by periods of derision. This time, however, belief in the transformative power of AI has penetrated the policy, media and administrative classes as thoroughly as the belief in apocalyptic climate change.

Todays AI develops an idea that has been around from the start. It uses multi-layered neural networks which calculate probabilities to find statistical regularities or patterns.

The field is rife with anthropomorphic metaphors: AI is undergoing training, for example, or deep learning. But these terms are really misdirections, for the software has acquired no knowledge or understanding of the underlying data it is processing. Instead, the software has bludgeoned its way through a task using brute force producing a statistical approximation to achieve a result.

A better name for the various activities currently undertaken by AI may be heuristic software. But then this might remind us that its guesswork, and that things can go wrong. Sometimes this guesswork can be impressive. At other times it is sufficient to be useful. Often it is not, and AIs ignorance of the real world can be painful, and hilarious.

But companies selling AI software or services claim a great deal more on AIs behalf. AI is one of the most important things humanity is working on, insists Sundar Pichai, CEO of Alphabet, Googles parent company. It is more profound than, I dunno, electricity or fire, he even claimed last year.

Our political elites accept such claims at face value, because it allows them to indulge in a little vanity. They can imagine themselves taking their place alongside the boffins, as visionaries or vanguardistas, as the future sweeps in. Five years ago, I was one of over 200 people and only three from the professional media invited to give oral evidence to a House of Lords inquiry into artificial intelligence. In advance, we were given nine points on which the lords might wish to hear our views. One of these was how we would prepare the population for the sweeping changes that were to come from new developments in AI. Apparently, as journalists we were not expected to question such improbable claims. It was taken as a given that AI would soon be a smashing success.

Five years on, the hype has reached new levels of absurdity, with artistic pastiches of models, like Open AIs GPT-3 language generator, being mistaken for human-like sentience.

The political class was promised a fourth industrial revolution, but AI is conspicuously failing to deliver tangible practical results. Yes, it is becoming another useful tool in the data-analytics toolbox. But it has failed to make an impact on other key areas, such as robotics, just as sceptical robotics scientists predicted.

Not one radiologist has been made redundant by AI, the neuroscientist and author Gary Marcus pointed out recently. Marcus has argued for some time that the current approach to AI has hit a wall, and is proving to have very little use outside the IT industry. AI remains extremely crude and dumb. For his pains, he finds himself in the same boat as so-called climate deniers. And with uncanny echoes of Climategate, the AI priesthood even refuses to allow researchers like Marcus to view or test the models themselves, in case they find something wrong with them. Nevertheless, the stunts and AI is a faith that requires regular miracles get ever more spectacular.

In fact, invoking religion or magic when flogging AI is not new. The original term was a triumph of marketing. A young professor called John McCarthy, who co-edited an obscure academic journal called the Journal of Automata Studies, decided that this new branch of mathematics could use some pizazz. Automata werent sexy enough. I invented it when we were trying to get money for a summer study, McCarthy would later admit.

The appeal of being God, of artificially giving birth, was something Professor Sir James Lighthill identified as one of AIs promises. Lighthill undertook the review that cancelled most of the funding for AI in 1973. Today, DeepMind the AI subsidiary of Alphabet is a master at evoking unexpected or creative outcomes supposedly produced by its deep-learning applications, which critics refer to as Its alive! moments. These tricks work spectacularly well with journalists, who are only too willing to suspend their scepticism. Such credulity is abundant, for example, in a long cover feature in The Economist this month, which marvels at the emergent properties of an AI that border on the uncanny.

Throughout those first and second AI summers, religious claims were never far away. During the second revival of AI in the 1980s, philosopher Mary Midgley lamented how dreary and familiar all the great claims about AI sounded to her.

They promise the human race a comprehensive miracle, a private providence, a mysterious saviour, a deliverer, a heaven, a guarantee of an endless happy future for the blessed who will put their faith in science and devoutly submit to it, she wrote in a review of a 1984 book by Professor Donald Michie, one of the leading British AI academics (Michie led one of the few departments to survive the 1970s AI winter). Is it clear why I was reminded of hymn books?, asked Midgley. Michie exhibited a crude indiscriminating euphoria, she wrote, and there is no better description of his successors 50 years later they too have a liturgical quality.

What AI shares with radical environmentalism is a longing to create an external moral arbiter. With apocalyptic climate change, the planet is judging us because we dared improve our lot. In AIs Jesuit wing transhumanism man hasnt fallen, we were just awful all along. Among transhumanists, there is a revulsion toward the physical body, which decays and defines a fixed form, and also a revulsion at what is characterised as our hopeless irrationality. We have always been inferior to the machines, they argue, but those machines just hadnt been invented yet. By submitting to the machines, we become free, as Grimes 2018 single, We Appreciate Power, articulates:

People like to say that were insaneBut AI will reward us when it reignsPledge allegiance to the worlds most powerful computerSimulation: its the future.

Here the religious overtones are explicit immortality is achieved by digitising the physical and uploading it. The deeply misanthropic idea that humans are not unique, and are in fact a bit rubbish, is not a new invention of the AI evangelists, of course. It has become commonplace in fields such as neuroscience and cognitive science to argue that consciousness is a trick of the mind, that the subjective self is an illusion or a trick of the brain circuitry. Cognitive scientist and philosopher Daniel Dennett was making this case three decades ago. A parallel, materialist view is even older: the proposition that were just poorly functioning machinery was expressed by Richard Dawkins in his 1976 bestseller, The Selfish Gene, where he wrote: You, dear human, are simply a gigantic lumbering robot.

In the early 2000s, computer pioneer and technology critic Jaron Lanier recognised these two beliefs as two cheeks of the same backside a backside he called cybernetic totalism. He was dismayed that so many highly intelligent friends of his in science and technology were sympathetic to this collection of prejudices, in part or in whole. Of the six characteristics he identified of this worldview, one was that subjective experience either doesnt exist, or is unimportant because it is some sort of ambient or peripheral effect. Subjectivity has long been unfashionable among the intelligentsia, as James Heartfield identified in The Death of the Subject Explained in 2002. Twentieth-century literary fashions like structuralism, cognitive science and more recently behavioural science merely added some intellectual respectability to these prejudices.

Two decades ago, Lanier already had an explanation for the supposedly magical and emergent properties of todays AI. To make the computers look smart, we have to make ourselves stupid, he observed. It requires a curious act of self-abasement. Unfortunately, abasing ourselves is a habit to which our elites seem strangely addicted. Hollowing out what it means to be human has cleared the path for both artificial intelligence and apocalyptic environmentalism, two of the most powerful religions of the 21st century.

Andrew Orlowski is a weekly columnist at the Daily Telegraph. Follow him on Twitter: @AndrewOrlowski.

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The myth of 'artificial intelligence' - spiked - Spiked