Category Archives: Ai

News item | 13-10-2022 | 11:38

Trade mission of 45+ Dutch businesses to London brings together AI and mobility innovators from across the Channel, who are taking technologies in these fields to new levels

As North Sea neighbours, the interests of the Netherlands and the UK in tackling climate change, as well as stimulating sustainable trade and investments are closely entwined. On 18 & 19 October, a Dutch trade mission to London consolidates this momentum, bringing together businesses, knowledge institutes and local and national governments. A Letter of Intent with a value of 250 million euro (218 million GBP) will be signed by Robots of London and Wemeetz to further exploit the potential of AI in remote (net)working. Dutch Minister for Foreign Trade and Development Cooperation, Liesje Schreinemacher, and Dutch Minister for Environment, Vivianne Heijnen, as well as International Trade Secretary, Kemi Badenoch, and other UK representatives will join the mission to share their insights.

The two-day event in London will focus on Artificial Intelligence (AI) for net zero and Sustainable Urban Mobility solutions. The trade mission comes at a relevant time for innovators from both countries, as the recently published Global Innovation Index 2022 has ranked the UK (fourth) and the Netherlands (fifth) as the worlds most innovative countries.

Karel van Oosterom: At the moment, both the UK and the Netherlands are facing strong economic headwinds. Many evolving challenges require international cooperation, such as the energy transition currently complicated by the Russian invasion in Ukraine or the effects of climate change. We have to keep in mind that entrepreneurial spirit can help us move forward in the transition to net zero. This mission will be about strengthening cooperation with UK innovators, as innovation plays a key role in future productivity growth and societally impactful progress in the field of clean technologies or mobility. According to the Dutch, driving innovation is all about collaboration.

This is the first in-person trade mission undertaken by the Dutch government following the EU-UK Trade Agreement. In the past two and a half years, entrepreneurs on both sides of the Channel have had to adapt continuously to the new way of doing business. Nonetheless, the UK and the Netherlands are and remain close trading partners. The Netherlands is the UKs 4th largest trading partner, accounting for 6.5% of total UK trade (DiT). Vice versa, the UK remains in the top 5 of the Netherlands closest trading partners (CBS). In 2021, the UK was the most important export destination for Dutch services after Germany and the US (CBS).

In the Netherlands, AI has been earmarked as one of the key innovation sectors. TheNetherlands AI Coalition(NL AIC) recently received a total investment of 1.05 billion euro (about 890 million pounds) from the Dutch National Growth Fund over the period 2021-2027. According toTechleap, about 650 AI startups and scale-ups are based in the Netherlands, making it the country with the highest density of AI startups in the EU per capita. In 2019, the worlds largest research network for AI,CLAIRE(Confederation of Laboratories for Artificial Intelligence Research in Europe), was established in The Hague. However, to exploit the potential of AI as a force for good, cross-border cooperation is needed.

Ranked first in Europe and third globally, the UK is a global frontrunner in the development and adoption of AI technologies. The UK has an extensive AI ecosystem consisting of the catapult centres, government bodies, outstanding research institutions, accelerator programmes and a rich diversity in AI-companies. Moreover, the UK has recognised AI as a gamechanger in reaching net zero. AI plays an important role in the sustainable energy transition by matching energy supply and demand more accurately. AI can also make industrial supply chains more sustainable by enabling predictive maintenance; creating digital twins and monitoring emissions more precisely. Dutch participants of the trade mission would like to work together with UK businesses and knowledge institutes on AI applications to help reach the goal of net zero in 2050.

The trade mission comes at an exciting time for the mobility sector. The Netherlands is known for its cycling culture, yet the Dutch government aims to shift to a higher gear with its recently published cycling policy. With a stimulus package of 50 million euro, it aims to get an extra 100,000 people commuting by bicycle over the next two and a half years in the interest of accessibility, public health andclean air. Following the launch of Active Travel England earlier this year and a widespread increase in cycling due to the Covid-19 pandemic, the volume of bicycles on the roads has increased. Sales soared to 70+% in 2020 and continue steadily, leading to a higher demand in secure and durable storing (as provided by FietsHangar) or intelligent solutions to increase safety in traffic (Sycada).

But theres more to sustainable mobility than the humble bicycle. In both the Netherlands and the UK, serious efforts are made in the roll out of charging facilities for electrical vehicles. The UK has the ambition to increase the number of charging facilities to 2500 in 2030 and 6000 in 2035. Dutch businesses like Leap24, providing these stations for vans and trucks servicing (ultra) low emission zones, are therefore looking forward to explore partnerships in the UK. During the trade mission, a micro mobility report by Royal HaskoningDHV will be presented to outline the UK market, as well as opportunities for Dutch businesses including as 5G development, e-cargo or last mile journeys.

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The Netherlands and the UK intensify cooperation on AI and mobility solutions - The Netherlands and You

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Global Call Centre AI Market

Global Call Centre AI Market

Dublin, Oct. 13, 2022 (GLOBE NEWSWIRE) -- The "Call Center AI Market with Covid-19 Impact Analysis, By Component, Mode of Channel (Phone, Social Media, & Chat), Application (Workforce Optimization & Predictive Call Routing), Deployment Mode, Vertical and Region - Global Forecast to 2027" report has been added to ResearchAndMarkets.com's offering.

The global Call Center AI Market size is to grow from USD 1.6 billion in 2022 to USD 4.1 billion by 2027, at a Compound Annual Growth Rate (CAGR) of 21.3% during the forecast period.

The services segment is projected to grow at a higher CAGR during the forecast period

The services segment is projected to grow at a higher CAGR during the forecast period. This can be attributed to the need for determining the time and cost required to install the solution that requires fully managed call center AI services. Call center AI solutions ensure the strengthening of customer relationships, resulting in increased first call resolution rate and improved customer experience.The large enterprises' segment will hold the larger market share during the forecast period

The large enterprise segment is estimated to hold a larger market share in 2022. Large enterprises focus on solutions to effectively manage complex business processes to enhance customer engagement. Hence, these organizations are using call center AI solutions to effectively manage complex operations. The SMEs segment is projected to register a higher CAGR during the forecast period due to the growing need to enhance business processes, reach new customers, stay competitive and control their spending.

The social media segment is to grow at the highest CAGR during the forecast period

The social media segment is estimated to grow at the highest CAGR during the forecast period due to the rising demand for social media used in sentiment analysis that helps understand customer perceptions about the brand. It also helps amplify customer service quickly through shares and likes. The phone mode of the channel is expected to hold the largest share in 2022 due to the rising penetration of smartphone users across the globe.The cloud segment is expected to hold larger market size during the forecast period

The cloud segment is expected to hold larger market size during the forecast period. The cloud technology benefit of easy deployment and minimal capital requirement facilitates the adoption of the cloud deployment model. The increasing demand for scalable, easy-to-use, and cost-effective solutions is expected to boost the demand of cloud-based call center AI in the market. Call Center AI solutions are expected to accelerate the growth of the cloud segment in the call center AI market. Moreover, cloud-based call center AI solutions enable business operations to improve employee productivity and save OPEX. Hence, the cloud-based deployment model is gaining traction in the coming years.The predictive call routing segment is expected to have the highest CAGR during the forecast period

The predictive call routing segment is expected to have the highest CAGR during the forecast period. The predictive call routing application ensures the highest possibility of first contact resolution (FCR) and prevents overburdening agents by adapting to their existing call queue in the call center AI market.The BFSI segment is expected to hold the largest market size during the forecast period

The BFSI segment is projected to hold the largest market size during the forecast period. The growth of this vertical is attributed to the increased adoption of call center AI solutions by financial institutions, which helps them flawlessly connect with customers, improve customer experience, and lowers customer churn. The growth of the segment is attributed to the rising need to protect businesses from costly regulatory litigations and reputational risks due to fraudulent activities while storing and managing customer information and serving customers.Among regions, APAC holds the highest CAGR during the forecast period

The market in the Asia Pacific is expected to grow at the highest CAGR during the forecast period due to the increasing penetration of advanced technologies, rising GDP of countries, and high density of contact center outsourcing operations. Asia Pacific consists of emerging economies, such as China, Japan, and India, where call center AI solutions are being deployed at a large scale due to the presence of a lot of data centers and a high density of BPO operations. Moreover, the demand for cloud-based call center AI solutions by business enterprises is expected to enhance the customer experience in the region.

Key Topics Covered:

1. Introduction

2. Research Methodology

3. Executive Summary

4. Premium Insights

5. Market Overview and Industry Trends5.1 Introduction5.2 Market Dynamics5.2.1 Drivers5.2.1.1 Advent of AI in Call Center to Offer Enhanced Customer Support Services and Better Experience5.2.1.2 Rising Development in Customer Engagement Through Social Media Platforms5.2.1.3 Increased Data Generation5.2.2 Restraints5.2.2.1 Unsupervised Learning5.2.3 Opportunities5.2.3.1 Advancements in AI and Ml to Facilitate Real-Time Actionable Insights5.2.3.2 Integration of Gesture Recognition with AI-Based Chatbots and Ivas5.2.4 Challenges5.2.4.1 Data Privacy and Security Concerns During Pandemic5.2.4.2 Lack of Skilled Workforce to Articulate Business Operations5.2.4.3 Preference for Online Chat Over Chatbots5.2.4.4 Slow Digitization Across Emerging Economies5.2.5 Cumulative Growth Analysis5.3 Industry Trends5.3.1 Call Center AI Market: Evolution5.3.2 Ecosystem5.3.3 Supply Chain Analysis5.3.4 Market: COVID-19 Impact5.3.5 Trends/Disruptions Impacting Buyers/Clients of Market5.3.6 Case Study Analysis5.3.6.1 Citibot Used Amazon Lex to Build Conversational Interfaces for Text and Voice Applications5.3.6.2 Osu University Used Amazon Connect and Qnabot to Provide Seamless Experience Across Voice and Chat for Customers and Agents5.3.6.3 Oscar Health Chose Cxone Workforce Management Enterprise to Minimize Administrative Burden and Focus on Scheduling and Forecasting5.3.6.4 Pldt Turned to Oracle Digital Assistant Running on Oracle Cloud Infrastructure to Power Its Self-Service Chatbot5.3.6.5 Echo Chose Oracle Digital Assistant to Help Improve Customer Experience5.3.6.6 Firefly Health Switched to Dialpad to Get New Information Quickly5.3.6.7 Solarzero Used Dialpad to Have a Modern Phone System That is Highly Reliable and Does Not Drop Calls5.3.6.8 Standard Chartered Used Avaya Onecloud to Achieve Personalized and Consistent Client Service5.3.6.9 Preferred Home Care Used Avaya Cloud Office to be Able to Reach Patients During an Outage or Other Crisis5.3.6.10 Vodafone Selected Amazon Connect to Simplify Contact Center Operations by Drawing on AI and Ml5.3.7 Technology Analysis5.3.8 Patent Analysis5.3.9 Pricing Model Analysis, 20215.3.10 Porter's Five Forces Analysis5.3.11 Scenario5.4 Regulatory Implications5.5 Key Stakeholders and Buying Criteria5.6 Key Conferences & Events in 2022-20235.7 Regulatory Landscape

6. Call Center AI Market, by Component6.1 Introduction6.1.1 Call Center AI: COVID-19 Impact6.2 Solutions6.2.1 Platform6.2.1.1 Rising Demand for AI in Call Centers to Enhance Agent Performance and Enable Customers6.2.2 Software Tools6.2.2.1 Call Center AI Software Helps in Deeper Understanding of Customers Across Different Contexts and Channel Modes6.3 Services6.3.1 Professional Services6.3.1.1 Training & Consulting Services6.3.1.1.1 Training and Consulting Services Help in Initial Phase of Implementing Call Center AI6.3.1.2 Support & Maintenance6.3.1.2.1 Support & Maintenance Services Help Organizations Understand Changing Business Conditions and Market Trends6.3.1.3 System Integration & Implementation Services6.3.1.3.1 System Integration & Deployment Services Facilitate Integration of Devices and Software and Their Deployment6.3.2 Managed Services6.3.2.1 Enterprises Must Ensure Provision of Certain Services for Their Clients to Maintain Their Market Position

7. Call Center AI Market, by Organization Size7.1 Introduction7.2 Large Enterprises7.2.1 Focus on Solutions to Effectively Manage Complex Business Processes to Enhance Customer Engagement7.3 Small and Medium-Sized Enterprises7.3.1 Reduced Operational Costs, Government Support, and Enhanced It Infrastructure to Influence Adoption of Call Center AI Solutions

8. Call Center AI Market, by Mode of Channel8.1 Introduction8.2 Phone8.2.1 Phone to be the Most-Used Customer Service Channel to Help Customers Get Quick Resolution for Their Queries8.3 Social Media8.3.1 Customers Use Social Media Platforms to Highlight Positive or Negative Experiences They Have Had with Brands8.4 Chat8.4.1 Chat-Based Call Center AI Software Tools to Improve Response Time and Lower Operational Costs in Long Run8.5 Email or Text8.5.1 Versatile Website Engagement Tool to Communicate Personal Correspondence and One-On-One Conversations8.6 Website8.6.1 Website Chat to be Most Cost-Effective Channel to Support Multiple Customers at One Time

9. Call Center AI Market Size, by Deployment Mode9.1 Introduction9.2 Cloud9.2.1 Cloud-Based Call Center AI Solutions to Gain Traction due to Their Cost-Effectiveness and Global Availability9.3 On-Premises9.3.1 Data Privacy Concerns and Increasing It Infrastructure Costs to Drive Growth of On-Premises Deployment Mode

10. Call Center AI Market, by Application10.1 Introduction10.2 Workforce Optimization10.2.1 Workforce Optimization to Modernize Call Center Technologies and Platforms10.3 Predictive Call Routing10.3.1 Predictive Call Routing to Use Artificial Intelligence-Based Call Center Techniques and Analytics10.4 Journey Orchestration10.4.1 Journey Orchestration to Provide Holistic View of Customer Interactions with Organization10.5 Agent Performance Management10.5.1 Growing Need to Manage and Handle Agent Performance to Drive Market Growth10.6 Sentiment Analysis10.6.1 Rising Need to Automate Contact Center Processes and Gain Customer Insights to Boost Call Center AI Growth10.7 Appointment Scheduling10.7.1 Need to Automate Multiple Tasks and Enhance Personalized Customer Experience to Drive Market Growth10.8 Other Applications

11. Call Center AI Market, by Vertical11.1 Introduction11.2 Banking, Financial Services, & Insurance11.3 Media & Entertainment11.4 Retail & Ecommerce11.5 Healthcare & Life Sciences11.6 Travel & Hospitality11.7 It & Telecom11.8 Transportation & Logistics11.9 Other Verticals

12. Call Center AI Market, by Region

13. Competitive Landscape

14. Company Profiles14.1 Introduction14.2 Key Players14.2.1 IBM14.2.2 Microsoft14.2.3 Oracle14.2.4 Aws14.2.5 Google14.2.6 Sap14.2.7 Avaya14.2.8 Nice14.2.9 Nuance Communications14.2.10 Genesys14.2.11 8X814.2.12 Artificial Solutions14.3 Other Players14.3.1 RingCentral14.3.2 Talkdesk14.3.3 Dialpad14.3.4 Twilio14.3.5 Zendesk14.3.6 Five914.3.7 Kore.AI14.3.8 Inbenta14.3.9 Creative Virtual14.4 Startups/SMEs14.4.1 Haptik14.4.2 RulAI14.4.3 Pypestream14.4.4 Avaamo14.4.5 Senseforth.AI14.4.6 Observe.AI14.4.7 Yellow.AI14.4.8 Ultimate.AI14.4.9 Cognigy

15. Adjacent and Related Markets

16. Appendix

For more information about this report visit https://www.researchandmarkets.com/r/7ewan0

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The Worldwide Call Center AI Industry is Expected to Reach $4.1 Billion by 2027 - Yahoo Finance

Adaptive AI can be used to continually improve the customer experience with each iteration of an interaction between a customer and a brand.

Adaptive Artificial Intelligence (AI) is able to update its own code to incorporate what it has learned from its experiences with new data. This means Adaptive AI can be used to continually improve the customer experience with each iteration of an interaction between a customer and a brand. AI-driven chatbots will continually refine their conversational skills, and recommendation engines will become more refined, making recommendations that truly resonate with customers. Lets look at adaptive AI, the ways brands are using it today, and the future of adaptive AI for improving the customer experience.

The traditional machine learning (ML) model consists of training and prediction pipelines. A pipeline can be thought of as an interconnected and streamlined collection of operations. The training pipeline aggregates and ingests data throughout the various stages of data cleaning, grouping and transformation. The prediction pipeline then analyzes the data to generate accurate insights and predictions that will be used for fruitful decision making.

Adaptive AI, on the other hand, consists of a single pipeline that monitors and learns the new changes made to the input and output values and their associated characteristics. Additionally, it learns from the events that may change the behavior of consumers and businesses in real time and is able to consistently maintain its accuracy. Adaptive AI incorporates the feedback it has received from the operating environment and then uses it to create data-informed predictions. This allows for super-fast solutions for the verification of ideas and simple deployment functionality in production.

Mike Gozzo, chief product officer at Ada, an AI-based automated brand platform provider, told CMSWire adaptive AI relies on the regular training and extension of ML and Natural Language Understanding (NLU) capabilities, and said this will increase the quality of CX. It works best when trained on millions, or even billions, of customer interactions across different geographies, industries and use cases, Gozzo said. This creates a rich data set that drives personalized and proactive experiences for each customer, in every interaction. Gozzo explained when a global ML model is combined with brand-specific models, the time it takes to train conversational AI and increase reliability is reduced.

The three tenets of adaptive AI are said to be robustness, efficiency and agility.

Robustness refers to the ability of adaptive AI to accomplish high algorithmic accuracy.

Efficiency refers to the ability of adaptive AI to pull off low resource usage.

Agility refers to the capacity of adaptive AI to change operational conditions based on current requirements.

Synergistically, these three core elements of adaptive AI comprise the key metrics for extremely efficient AI-informed actions for many applications.

Ricardo Zuasti, chief product officer at Technisys, a leading global provider of next-generation digital banking platforms, told CMSWire being able to deliver truly tailored experiences customers will increasingly want and expect will depend on a business ability to fuel AI-powered decision making in every channel, in real time. Zuastis business is moving toward the use of AI to power context and behavior-sensitive mechanisms beyond conversational interactions but more broadly to adapt the user experience dynamically to what the person needs and wants in near real time.

Related Article: If You Want to Succeed With Artificial Intelligence in Marketing, Invest in People

AI-driven chatbots are commonly used on websites as a way for customers to instantly locate the goods or services they are searching for, as well as for customer service needs. Adaptive AI is still an emerging technology. However, there are already chatbots using it to enhance the chat experience.

Hyro, for instance, offers an adaptive AI-driven chatbot being used in healthcare, real estate and government industries. Hyro automatically scrapes a variety of data sources including websites, databases, application programming interfaces (APIs) and more, and when content is updated, the conversation is also updated. The unstructured data is mapped to a knowledge graph made to be queryable by Natural Language Processing (NLP).

Adam Dorfman, vice president (VP) of product at Reputation, an online reputation management solution provider, told CMSWire through adaptive AI and ML, an AI-powered chatbot can continually improve service in a number of ways without human intervention. For instance, a chatbot using adaptive AI can improve the accuracy of its replies and learn how to give more personalized responses based on each customers needs (instead of generic, pre-formulated answers), adding Adaptive AI also promises to help chatbots become more human and accessible by learning to give answers in a more natural way by improving a chatbots conversational skills. This is important because when a chatbot can successfully emulate nuances of tone and language style, people are more likely to emotionally trust the customer experience theyre getting with a business.

Related Article: The Role of Journey Orchestration Engines in 2022

One of the areas where adaptive AI is shining is in edge computing.IBM defines edge computing as a distributed computing framework that physically locates applications closer to data sources such as Internet of Things (IoT) devices or local edge servers. A 2022 Gartner Report predicted by 2025, more than 50% of enterprise-managed data will be created and processed outside the data center or cloud using edge computing.

By using adaptive AI, edge systems are able to dynamically adjust their computing needs, effectively lowering compute and memory resource requirements. Adaptive AI allows edge applications to adapt and adjust to their workloads based on their requirements and environments. By using attention and context to use only the parts of its neural network it needs, adaptive AI is able to do the processing locally on edge devices. Adaptive AI is a new method for neural networks to operate that dynamically minimizes the amount of memory and compute horsepower required.

Brian David Crane, founder of Spread Great Ideas, a digital marketing fund, told CMSWire adaptive AI is the next big thing in automation and AI, as programs and machines become self-sustaining mechanisms that continually learn and adapt to human behavior. The self-driving car is based on adaptive AI and is a glaring example of how brands are using AI today. Brands like Amazon, Netflix and Google are already using adaptive AI to provide a better user experience, said Crane.

Companies are exploring how they can use adaptive AI to deliver customized learning solutions to students based on their individual learning capacities and behavior, said Crane, who added cybersecurity companies are also exploring adaptive AI to create an automated self-sustaining protocol that learns and models itself with continual iterations to fight digital threats and cyberattacks in real time.

The implications of adaptive AI on customer experience are vast and game-changing. By analyzing social, behavioral and past interactions, adaptive AI uses continuous interactions to predict and anticipate customer behavior and provide highly personalized solutions to improve the customer journey and deliver positive CX, said Crane, who explained adaptive AI focuses on feelings and emotions and analyzes sentiments to create perfect interactions on a real-time basis.

As an example of such an experience, Crane said to think of hyper-interactive and customized displays at retail points that use facial interpretations, voice analysis and body language analysis to identify shoppers' emotions and mindsets and offer solutions in real-time to deliver a positive experience.

Adaptive AI will enable amazing customer experiences that will be unique, positive, and emotionally connected. With adaptive AI, businesses can predict the next buyer experience and offer personalized recommendations, discounts and individualized offers, thus helping build an emotional connection with the brand through these experiences over time, said Crane.

Adaptive AI takes AI-driven chatbots to the next level through the use of ML, NLU, NLP and real-time decision making, and has the potential to improve the customer experience in real time, enhancing hyper-personalization and recommendation engines, and improving edge computing by minimizing compute and memory resource requirements.

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Adaptive AI Will Improve the Customer Experience - CMSWire

NEW YORK--(BUSINESS WIRE)--Today, Perfect Corp., the leading artificial intelligence (AI) and augmented reality (AR) beauty and fashion tech solutions provider, introduced its new virtual try-on technology for hairstyles. The innovative solution leverages advanced AI technology, including machine learning and Generative Adversarial Network (GAN), to help brands and hair salon businesses provide customers with hyper-realistic hairstyle simulations. This new virtual try-on solution will help consumers visualize how various haircuts look before committing to a certain style, ultimately helping to increase consumer confidence while personalizing the salon experience in an exciting new way.

Virtual Try-on Tech for Hairstyles Provide Consumers with True-to-Life Previews of 12 Hairstyle Makeovers, Including Hair Color

Perfect Corp.s AI virtual try-on technology for hairstyles allows consumers to experiment with different looks before committing to a new haircut. Users can choose from 12 unique styles, including classic bob cut, bob cut without bangs, curly bob cut, wavy bob cut, short, pixie cut, long curly, long wavy, long wavy with bangs, long straight with bangs, comb over, and buzz cut. The experience is further enhanced with AR hair color try on, delivering a comprehensive suite of hairstyle AR solutions. Featured styles can also be viewed through before and after simulations, helping consumers accurately visualize their next hair transformation. The solution takes the stressful guesswork out of the hairstyle consultation experience, and helps consumers feel confident about their next salon visit.

Cutting-Edge AI, ML, and GAN Technology Ensures Hyper-Realistic Hairstyle Simulations

Perfect Corp.s AR virtual try-on technology for hairstyles leverages highly-sophisticated AI technology, using the latest developments in machine learning and Generative Adversarial Network setups, to guarantee true-to-life hairstyle simulations. When simulating transitions to shorter haircuts and styles, the advanced AI algorithm is capable of recreating parts of the face, ears, neck, and head to ensure a realistic representation of the end result. The technology also takes hair color, and skin tone into consideration when adapting simulations to each consumers unique characteristics, delivering inclusive and impactful AI simulations for all.

Transforming the Hairstyle Consultation Experience with Digital Tech Innovations

Our goal is to solve consumer pain points through the power of AI and AR technology innovations, said Perfect Corp. CEO and Founder Alice Chang, As beauty consumers look to brands and hair salon businesses to provide more personalized and immersive client experiences, Perfect Corp.s AR virtual try-on solution for hairstyles will offer a game-changing tool to supercharge customer satisfaction.

To learn more about Perfect Corp.s AR Virtual Try-On Tool for Hairstyles, please visit: https://www.perfectcorp.com/business/products/virtual-hairstyles

About Perfect Corp.

Perfect Corp. is the leading SaaS AI and AR beauty and fashion tech solutions provider, dedicated to transforming shopping experiences through empowering brands to embrace the digital-first world. By partnering with the largest names in the industry, Perfect Corp.s suite of enterprise solutions deliver synergistic, technology-driven experiences that facilitate sustainable, ultra-personalized, and engaging shopping journeys, as well as equipping brands with next generation of consumer goods. Perfect Corp. offers a complementary suite of mobile apps, including YouCam Makeup and YouCam Perfect, to provide a consumer platform to virtually try-on new products, perform skin diagnoses, edit photos, and share experiences with the YouCam Community. To learn more, please visit PerfectCorp.com.

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New AI GAN-Powered Virtual Try-on for Hairstyles Solution from Perfect Corp. is Poised to Elevate Hair Salon Experience - Business Wire

For instance, TaylorMade Golf Company turned to Microsoft Syntex for a comprehensive document management system to organize and secure emails, attachments and other documents for intellectual property and patent filings. At the time, company lawyers manually managed this content, spending hours filing and moving documents to be shared and processed later.

With Microsoft Syntex, these documents are automatically classified, tagged and filtered in a way thats more secure and makes them easy to find through search instead of needing to dig through a traditional file and folder system. TaylorMade is also exploring ways to use Microsoft Syntex to automatically process orders, receipts and other transactional documents for the accounts payable and finance teams.

Other customers are using Microsoft Syntex for contract management and assembly, noted Teper. While every contract may have unique elements, they are constructed with common clauses around financial terms, change control, timeline and so forth. Rather than write those common clauses from scratch each time, people can use Syntex to assemble them from various documents and then introduce changes.

They need AI and machine learning to spot, Hey, this paragraph is very different from our standard terms. This could use some extra oversight, he said.

If youre trying to read a 100-page contract and look for the thing thats significantly changed, thats a lot of work versus the AI helping with that, he added. And then theres the workflow around those contracts: Who approves them? Where are they stored? How do you find them later on? Theres a big part of this thats metadata.

The availability of DALLE 2 in Azure OpenAI Service has sparked a series of explorations at RTL Deutschland, Germanys largest privately held cross-media company, about how to generate personalized images based on customers interests. For example, in RTLs data, research and AI competence center, data scientists are testing various strategies to enhance the user experience by generative imagery.

RTL Deutschlands streaming service RTL+ is expanding to offer on-demand access to millions of videos, music albums, podcasts, audiobooks and e-magazines. The platform relies heavily on images to grab peoples attention, said Marc Egger, senior vice president of data products and technology for the RTL data team.

Even if you have the perfect recommendation, you still dont know whether the user will click on it because the user is using visual cues to decide whether he or she is interested in consuming something. So artwork is really important, and you have to have the right artwork for the right person, he said.

Imagine a romcom movie about a professional soccer player who gets transferred to Paris and falls in love with a French sportswriter. A sports fan might be more inclined to check out the movie if theres an image of a soccer game. Someone who loves romance novels or travel might be more interested in an image of the couple kissing under the Eiffel Tower.

Combining the power of DALLE 2 and metadata about what kind of content a user has interacted with in the past offers the potential to offer personalized imagery on a previously inconceivable scale, Egger said.

If you have millions of users and millions of assets, you have the problem that you simply cant scale it the workforce doesnt exist, he said. You would never have enough graphic designers to create all the personalized images you want. So, this is an enabling technology for doing things you would not otherwise be able to do.

Eggers team is also considering how to use DALLE 2 in Azure OpenAI Service to create visuals for content that currently lacks imagery, such as podcast episodes and scenes in audiobooks. For instance, metadata from a podcast episode could be used to generate a unique image to accompany it, rather than repeating the same generic podcast image over and over.

RTL Deutschland, Germanys largest privately held crossmedia company, is exploring how to use DALLE 2 in Azure OpenAI Service to engage people browsing its streaming service RTL+. One idea is to use DALLE 2 to generate unique images to illustrate individual podcast episodes, rather than relying on the same podcast cover art.

Along similar lines, a person who is listening to an audiobook on their phone would typically look at the same book cover art for each chapter. DALLE 2 could be used to generate a unique image to accompany each scene in each chapter.

Using DALLE 2 through Azure OpenAI Service, Egger added, provides access to other Azure services and tools in one place, which allows his team to work efficiently and seamlessly. As with all other software-as-a-service products, we can be sure that if we need massive amounts of imagery created by DALLE, we are not worried about having it online.

No AI technology has elicited as much excitement as systems such as DALLE 2 that can generate images from natural language descriptions, according to Sarah Bird, a Microsoft principal group project manager for Azure AI.

People love images, and for someone like me who is not visually artistic at all, Im able to make something much more beautiful than I would ever be able to using other visual tools, she said of DALLE 2. Its giving humans a new tool to express themselves creatively and communicate in compelling and fun and engaging ways.

Her team focuses on the development of tools and techniques that guide people toward the appropriate and responsible use of AI tools such as DALLE 2 in Azure AI and that limit their use in ways that could cause harm.

To help prevent DALLE 2 from delivering inappropriate outputs in Azure OpenAI Service, OpenAI removed the most explicit sexual and violent content from the dataset used to train the model, and Azure AI deployed filters to reject prompts that violate content policy.

In addition, the team has integrated techniques that prevent DALLE 2 from creating images of celebrities as well as objects that are commonly used to try to trick the system into generating sexual or violent content. On the output side, the team has added models that remove AI generated images that appear to contain adult, gore and other types of inappropriate content.

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From Hot Wheels to handling content: How brands are using Microsoft AI to be more productive and imaginative - The AI Blog - Microsoft

As companies look to build diverse workforces, the biggest problem seems to be sourcing candidates from historically underrepresented groups, often because companies dont know where to look. TaTio, an Israeli startup from a couple of HR veterans, has built a platform to help connect companies to candidates who have the skills, but may lack a traditional resume.

Today the company announced a $5.3 million seed investment.

TaTio CEO Maya Huber says she and her co-founder, COO Mor Panfil, have over a decade of experience running HR companies. They personally experienced the frustration of trying to place people from a variety of underrepresented backgrounds, and the biggest problem was getting candidates through the resume review stage.

Every solution out there was still relying on resumes as a first step for people to apply. And we thought that there must be a different way because through the years as we worked with different types of underrepresented populations, we found that the resumes often did not reflect a persons actual skills, Huber explained.

The company developed a solution to help companies fill open positions with workers who are qualified, but lack traditional credentials. These hidden workers are disproportionately from underrepresented groups. TaTiO sources and vets job seekers with their AI work experience simulations and provides employers with pre-qualified candidates to interview, she said.

The company has built a two-sided marketplace. On one side they source candidates and then build tests that simulate the job the person is applying for. The idea is if the person performs well enough, they should be able to do the job. But the important thing is that it gets them over the resume hurdle.

We track candidates and we engage them through tests that simulate the core tasks of the jobSo if youre applying for a job as a sales representative, you will have 20 minutes to close three deals. We give you a pipeline of leads you need to qualify and create interactions with prospective clients, she said.

Using an underlying machine learning model, they judge the candidates performance and give them a score, and then match the candidates to the job openings with the goal of placing them in a job.

The company says that these models should improve over time as they collect more data on each simulation type. For now, the goal is to find a person who will be a good match for an open job with a particular employer, but in the future the company hopes to surface skills beyond what was explicitly being tested for.

They find candidates from a variety of sources including partnerships with NGOs and continuing education and training programs that work with underprivileged populations.

The challenge will be measuring the results. Its difficult to know how many of the candidates are from underrepresented groups unless they self-report. The hope is that by working with the companies using their service, they will be able to collect more data on the connection between their diversity hiring success, and the use of the TaTio platform. Huber says the companies using the platform are already reporting improvements, with some increasing their diversity hiring by 25% since they have been using TaTio.

The company currently has 16 employees, with over 75% women, although they are still trying to get more women in the engineering group. They also have age diversity, with some employees over 50 and some just starting their careers. So they are trying to practice what they preach in terms of diversity.

Todays seed round was led by Mensch Capital Partners and Cresson Management, with help from Cerca Discovery, Tau Ventures Ltd., Techstars, GoodCompany and a number of other industry angels.

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TaTio AI-based work simulations help diverse job seekers showcase their skills - TechCrunch

Politicians are often accused of giving robotic answers when facing questions - but politicians in the UK may just have been shown how its really done.

The android Ai-Da, which is claimed to be the worlds first ultra-realistic AI robot artist, was questioned by a committee in the British parliament on Tuesday.

The politicians from the Communications and Digital Committee in the House of Lords asked the robot - named after the 19th century computer pioneer Ada Lovelace - about the relationship between artificial intelligence, robots, and the arts.

"I do not have subjective experiences despite being able to talk about where I am and depend on computer programmes and algorithms who are very not alive. I can still create art," said the robot.

Ai-Da was created in collaboration with robotic creators Engineered Arts, with AI algorithms created by experts at the University of Oxford.

"The robot is providing evidence, but it is not a witness in its own right. And I don't want to offend the robot, but it does not occupy the same status as a human and that you as its creator, are ultimately responsible for the statements," said Tina Stowell, Baroness Stowell of Beeston, to Aidan Meller, Ai-Das creator.

Ai-Da can "see" with the help of two cameras, one in each eye, which is connected to a computer vision system that is then interrogated by an AI algorithm.

Meller said he learned a lot while developing the project.

"The biggest thing that I've seen, which absolutely takes me to my core, is actually not so much about how human-like Ai-Da is, but how robotic we are. The algorithms that run our systems are extremely able to be analysed, understood, and created," he said.

Thanks to her cameras, Ai-Da is able to do portraits of people that stand in front of her.

She can also draw the faces of people if engineers upload a picture for her.

"The role of technology in creating art will continue to grow as artists find new ways to use technology to express themselves and reflect and explore the relationship between technology, society, and culture," Ai-Da told the committee.

The team behind Ai-Da hopes that the robot will provoke discussions about the future of artificial intelligence and robotics.

Ai-Da is the first robot ever to appear before the UK's House of Lords, the upper house of the UK parliament.

In 2018, Pepper the robot gave evidence to a House of Commons education committee.

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Robot Ai-Da becomes first to give evidence to UK's House of Lords - Euronews

An interdisciplinary project has received funding to help ensure that if or more likely, when certain imaging tools that use artificial intelligence (AI) are put to clinical use, their inherent uncertainty is considered as part of any subsequent clinical decisions, including guiding treatment.

The project, led by Abhinav Jha, assistant professor of biomedical engineering at Washington University in St. Louis McKelvey School of Engineering, will be funded by a $314,807 grant from the National Institute of Biomedical Imaging and Bioengineering, part of the National Institutes of Health (NIH).

The two-part project will involve first developing a technique by which to quantify the amount of uncertainty in AI-based tools used to measure quantitative parameters from patient images, such as tumor volume.

Jha then will work with a team including Anya Plutynski, professor of philosophy in Arts & Sciences. The two worked together previously to better understand patients attitudes toward AI. For this project, they also will work with radiation oncologist Clifford Robinson, MD,and nuclear medicine physician Tyler Fraum, MD, both at Washington University School of Medicine, to develop a questionnaire for patients that will determine how much risk someone is comfortable with if using an AI-based tool as part of the clinical decision-making process, given the certainty of the measurement the tool provides.

Original post:

NIH grant supports Jha's work on ethics of AI in imaging - The Source - Washington University in St. Louis

Sarah Guo isnt late to the AI party, but she did just raise a $101 million fund to bet on the appetizers.

Hello and welcome back toEquity, a podcast about the business of startups, where we unpack the numbers and nuance behind the headlines. This is our Wednesday show, where we niche down to a single person, think about their work and unpack the rest. This week, Natasha and Alex interviewed Guo, who worked at Greylock for nearly a decade, and her launch of Conviction.

We spoke about the self-correcting venture market, what made her leave Greylock and even rewound to her last episode with us (recorded almost exactly three years ago).

There was also an especially fruitful conversation about the opportunity in artificial intelligence right now, and how shes defining Software 3.0. (Warning: We talk about SaaS!) We also dug into why she started a fund, the LP market and more. The conversation ran a bit long, but it felt reasonable to keep going given the sheer breadth of stuff that we wanted to get through.

Dont forget that Equity is going to be live at Disrupt next week, on Tuesday morning. Its going to be a blast. And before we go, two programming notes (that help your wallet, too):

Equity drops every Monday at 7 a.m. PT and Wednesday and Friday at 6 a.m. PT, so subscribe to us onApple Podcasts, Overcast, Spotify andall the casts. TechCrunch also has agreat show on crypto, ashow that interviews founders, ashow that details how our stories come togetherand more!

Originally posted here:

Sarah Guo isnt late to the AI party - TechCrunch

The recent years have witnessed immense technology adoption in the banking sector in the wake of the pandemic and during the race to economic recovery. Digitisation of banking services allowed institutional resilience and enabled banks to offer services and solutions online, reaching a broader customer base. The demand for a digital banking experience is transforming the entire banking industry, giving rise to innovative solutions and enhanced customer experience.

As banking embraces technology, hyperautomation technologies play a massive role in digitizing independent processes, improving the quality of services, and transforming operations. Beyond improving independent tasks, the connected technologies add efficiency to overall operations bringing together separate processes and departments to develop innovative solutions and industry-wide impact. Artificial Intelligence (AI) and Low-code play a crucial role in transforming banking by providing flexibility and reduced turnaround time to deliver faster solutions and efficient process implementation at a minimal investment compared to infrastructural investments to attain similar results.

While AI enables the digital workforce to replicate human intelligence to complete tasks performed by humans or require human intelligence at a rapid pace, Low-code development simplifies coding and application building to provide easy drag and drop functionality, making application development more accessible and faster than ever before. The technologies play a significant role in building the foundations of the future of banking as they implement solutions within shorter turnaround times without compromising efficiency while improving the customer experience. Here are a few ways in which AI and Low-code can transform the future of banking.

During the pandemic, when it was necessary to ensure remote working and social distancing, the digital workforce powered by AI ensured business continuity and productivity without affecting existing functions. Access to digital banking and remote services shaped the future of banking by developing innovative solutions to meet customer demands. Further, the inclusion of the digital workforce enables handling high volumes of operations to support diverse customers and stakeholders while ensuring the job security of the humans.

Previously, the onboarding of new customers and creation of their accounts or the application for new loans could take customers and institutions multiple steps and time-consuming manual reviews. However, with the inclusion of just one, if not all of the following hyperautomation technologies: low-code business rule-driven processes, robotic process automation (RPA), Optical Character Recognition (OCR), and artificial intelligence (AI), these time-consuming and manual processes have become, in some cases, real-time decision events. These improved customer experiences have allowed some institutions to prove the importance of the investment and set themselves apart from competitors by embracing the technologies.

Enhanced securityThe banking sector is constantly exploring ways to withstand cyberattacks, prevent data leaks, detect fraud, and ensure maximum security for operations. Technology implementation allows more control over data and access to live data to make informed decisions. Many organizations have turned to artificial intelligence processes to more accurately detect fraudulent activity and low-code processes to inform and receive feedback from customers in these instances.

Further, errors and omissions can be minimized, saving time and resources. Time-consuming audits and reviews can be automated, allowing round-the-clock monitoring and validating of records to ensure high levels of transparency, trust, and accountability.

User experience and customer supportLeveraging hyperautomation technologies have enhanced onboarding and backend functions improving customer experience. AI on the front end to smooth customer identification and authentication, mimic live employees through chatbots and voice assistants, deepen customer relationships, and provide personalized insights and recommendations. The digital workforce can handle customer support around the clock and position the human workforce to focus on relationship-building and cognitive roles.

Robotic process automation (RPA) may also be leveraged to help provide a streamlined audit trail of transactions and requests for increased reporting accuracy and transparency. Hyperautomation technologies allow institutions to support customers case-to-case basis with their highly flexible and customizable solutions.

Innovative solutions and complianceThe adoption of low-code and Artificial intelligence technologies has allowed a substantial increase in innovation within the financial services and banking industries. These technologies have allowed for increased integration of existing and secure customer data with new digital-first processes that can be created and introduced at exceptionally fast speeds, thanks to low-code options. As the industry transitions, many of these processes need to focus on increased compliance needs. Low-code platforms have allowed organizations to quickly implement business-rule-driven processes while enabling artificial intelligence to enforce them. They also encourage human intervention at any dispute or point where confidence in the AI decision may be required. These rules begin to work in concert, giving the organization increased processing speed and transparency into each customer transaction and improving customer experience.

As technology adoption shapes the digital transformation journeys of banks and financial institutions, it has also begun shaping customer experience and expectations. Remote banking, shortened transaction processing times, and increased security are all customer expectations stemming from the introduction of hyperautomation technologies in the industry. We do not foresee the pace of change reducing anytime soon.

(The writer is the Director of Services and Operations at Vuram)

Read more from the original source:

How AI and Low-code can transform the banking sector - Economic Times