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Among the many drivers of the tech ecosystems rapid growth, artificial intelligence (AI) and its subdomains are at the fore. Described by Gartner as the application of advanced analysis and logic-based techniques to simulate human intelligence, AI is an all-inclusive system with numerous use cases for individuals and enterprises across industries.

There are many ways of leveraging AI to support, automate and augment human tasks, as seen by the range of solutions available today. These offerings promise to simplify complex tasks with speed and accuracy, and to spur new applications that were impractical or possible previously. Some question whether the technology will be used for good or perhaps become more effective than humans for certain business use cases, but its prevalence and popularity cannot be doubted.

AI software can be defined in several ways. First, a lean description would consider it to be software that is capable of simulating intelligent human behavior. However, a broader perspective sees it as a computer application that learns data patterns and insights to meet specific customer pain points intelligently.

The AI software market includes not just technologies with built-in AI processes, but also the platforms that allow developers to build AI systems from scratch. This could range from chatbots to deep and machine learning software and other platforms with cognitive computing capabilities.

To get a sense of the scope, AI encompasses the following:

These capabilities are leveraged to build AI software for different use cases, the top of which are knowledge management, virtual assistance and autonomous vehicles. With the large volumes of data that enterprises must comb through to meet customer demands, theres an increased need for faster and more accurate software solutions.

As expected, the rise in enterprise-level adoption of AI has led to accelerated market growth of the global AI software market. Gartner places the growth at an estimated $62.5 billion in 2022 a 21.3% increase on its value in 2021. By 2025, IDC projects this market to reach $549.9 billion.

Whether it powers surgical bots in healthcare, detects fraud in financial transactions, strengthens driver assistance technology in the automotive industry or personalizes learning content for students, the overarching purpose of AI solutions can be grouped into four broad functional categories, including:

The automation function of AI applications meets AIs primary objective to minimize human intervention in executing tasks, whether mundane and repetitive or complex and challenging. By collecting and interpreting volumes of data fed into it, an AI solution can be leveraged to determine the next steps in a process and execute it seamlessly. It does this by leveraging the capabilities of ML algorithms to create a knowledge base of structured and unstructured data.

Process automation remains a top enterprise concern, with one survey exhibiting that 80% of companies expect to adopt intelligent automation in 2027.

A core function of AI solutions, especially for enterprises, is to create knowledge bases of structured and unstructured data and then analyze and interpret such data before making predictions and recommendations from its findings. This is called AI analytics and it uses machine learning to study data and draw patterns.

Whether the analytic tools are predictive, prescriptive, augmented, or even descriptive, AI is at the center of determining how the data is prepared, discovering new insights and patterns and predicting business outcomes. Enterprises are also turning to AI for improved data quality.

Building a relationship has become the holy grail of customer acquisition and retention. A study from McKinsey shows that one sure way to do this is through personalization and engagement. AI technologies allow enterprises to make personalized offers to customers and predict and solve their concerns in real-time. This function manifests in programs like conversational chatbots and product recommendations generated from learned customer behavior.

Many organizations are still getting up to speed with the technology. Gartner reports that 63% of digital marketers struggle to maximize personalization technology. Their survey of 350 marketing executives revealed that only 17% are actively using AI and ML solutions across the board, although 83% believe in its potency.

Along with greater automation of traditional processes, AI enables new services and capabilities that were not previously feasible. From driverless vehicles and natural language services for consumers to medical breakthroughs that could only have been imagined previously, AI is becoming the base of new products and markets that will continue to unfold.

Also read: Creating responsible AI products using human oversight

AI software solutions include general platforms for supporting a range of applications and products for more narrow, industry-specific use cases. We include a sampling of both in the following representative list. With 56% of organizations adopting AI for at least one business function, there are many options on the market today.

Below are ten examples of AI software solutions available in 2022.

Googles dominant cloud offering includes assorted tools to support developer, data science and infrastructure use cases. Several speech and language translation tools, vision, audio and video tools and deep and machine earning capabilities bring AI functionality to skilled technology practitioners and mass consumer markets. Google was named a leader in Gartners Magic Quadrant for Cloud AI Developer Services in 2022.

Like Google, IBM offers a platform for building and training AI software. The IBM Watson Studio provides a multicloud architecture for developers, data scientists and analysts to build, run and manage AI models collaboratively. With capabilities ranging from AutoAI to explainable AI, DL, model drift, modelops and model risk management, the studio gives subject-matter experts the tools they need to either gather and prepare data or create and train AI models.

It also allows these professionals the flexibility to deploy AI models on either public or private cloud (IBM Cloud Pak, Microsoft Azure, Google Cloud, or Amazon Web Services) and on-premises. IT teams can open source these models as they build them with embedded Waston tools like the Natural Language Classifier. Its hybrid environment may also provide developers with more data access and agility.

Named a leader in Gartners Magic Quadrant for CRM Customer Engagement Center thirteen times in a row and the #1 CRM solution for eight consecutive years by the International Data Corporation (IDC), Salesforce provides an advanced kit of sales, marketing and customer experience tools. Salesforce Einstein is an AI product that helps companies identify patterns in customer data.

This platform has a set of built-in AI technologies supporting the Einstein bots, prediction builder, forecasting, commerce cloud Einstein, service cloud Einstein, marketing cloud Einstein and other functions. Users and developers of new and existing cloud applications can also deploy the platforms predictive and suggestive capabilities into their models. For example, at Salesforce Einsteins launch in 2016, John Ball, general manager at Einstein, revealed that by creating Einstein, the company enables sales professionals to find better prospects and close more deals through predictive lead scoring and automatic data capture to convert leads into opportunities and opportunities into deals.

Oculeus provides an industry-specific solution. For service providers, network operators and enterprises in the telecom industry that need to protect and defend their communication infrastructure against cyber threats, Oculeus offers a portfolio of software-based solutions that can help them better manage network operations. According to founder and CEO Arnd Baranowski, Oculeus uses AI and automation to learn about an enterprises regular communications traffic and continually monitor it for exceptions to a baseline of expected communications activities. With its AI-driven technologies, suspicious traffic can be identified, investigated and blocked within milliseconds. This is done before any significant financial damage is caused to the enterprise and protects the brand reputation of the telecoms service provider.

The Communications Fraud Control Association (CFCA)s 2021 survey of international telecommunication fraud loss discovered losses amounting to over $39.89 billion, a 28% increase in losses over the previous year. Similarly, network security and operators are experiencing more fraud threats and attacks.

Among other things, these insights amplify the need for enterprises to switch to a proactive defense approach that outwits adversaries, and this what Oculeus claims to provide with its AI-powered telecoms fraud protection solutions. In Baranowskis words, Oculeus AI-driven approach to telecoms fraud protection does not only stop fraudulent telecommunications traffic before any significant financial damage is caused but also includes extensive automation tools that weed out threats thoroughly.

Edsoma represents another narrow use case. Its AI-based reading application software features real-time, exclusive voice identification and recognition technology designed to uncover the strengths and weaknesses in childrens reading. This follow-along technology identifies users spoken words and speaking speed to determine if they are saying the words correctly. A correction program helps put them back on track if they mispronounce something.

As Edsoma founder and CEO Kyle Wallgren explained, once the electronic book is read, the childs voice is transcribed in real-time by the automated speech recognition (ASR) system and immediate results are provided, including pronunciation assessment, phonetics, timing and other facets. These metrics are compiled to help teachers and parents make informed decision.

This technology aims to improve childrens oral reading fluency skills and provide them the necessary support to inculcate a healthy reading culture. Edsoma seeks to establish a share of the $127 billion global edtech market. By leveraging real-time data to provide real-time literacy, Edsoma looks to provide future-focused learning powered by AI.

Appen has been one of the early leaders as a source for data required throughout the development lifecycle of AI products. This platform provides and improves image and video data, language processing, text and even alphanumeric data.

It follows four steps in preparing data for AI processing: the first step is data sourcing which offers automatic access to over 250 pre-labeled datasets then data preparation, which provides data annotation, data labeling and knowledge graphs and ontology mapping.

The third stage supports model building and development needs with the help of partners like Amazon Web Services, Microsoft, Nvidia and Google Cloud AI. The final step combines a human evaluation and AI system benchmarking, giving developers an understanding of how their modes work.

Appen boasts a lingual database of more than 180 languages and a global skill force of over 1 million talents. Of its many features, its AI-assisted data annotation platform is the most popular.

Cognigy is a low-code conversational AI and automation platform recently named a leader in Gartners 2022 Magic Quadrant for Enterprise Conversational AI platforms. As the need for more excellent customer experience (CX) intensifies, more enterprises rely on conversational analytics solutions that dive deep into its customers text and voice data and discover insights that inform smarter decisions and automate processes.

This is why Cognigy automates natural communication among employees and customers on multimodal channels and in over 100 languages. In addition, its technology allows enterprises to set up AI-powered voice and chatbots that can address customer concerns with human-like accuracy.

Cognigy also has an analytics feature Cognigy Insights that provides enterprises with data-driven insights on the best ways to optimize their virtual agents and contact centers. In addition, the platform allows users to either deploy the technology on the cloud or on-premises. Particularly praised by Gartner for its customer references, flexibility and sustainability, this platform helps businesses create new service experiences for customers.

Synthesis AIs solution generates synthetic data that allows developers to create more capable and ethical AI models. Engineers can source several well-labeled, photorealistic images and videos in deploying its models on this platform. These images and videos come perfectly labeled with labels ranging from depth maps, surface normals, segmentation maps, and even 2D/3D landmarks.

Virtual product prototyping and the chance to build more ethical AI with expanded datasets that account for equal identity, appearance and representations are also some of its product offerings. Organizations can deploy this technology across API documentation, teleconferencing, digital humans, identity verification and driver monitoring use cases. With 89% of tech executives believing that synthetic data would transform its industry, Synthesis.ais technology may be a great fit.

Tealiums data orchestration platform is positioned as a universal data hub for businesses seeking a robust customer data platform (CDP) for marketing engagement. This CDP provider offers a tray of solutions in its customer data integration system that allows businesses to connect better with their customers. Tealiums offerings include a tag management system for enterprises to track and unify its digital marketing deployments (Tealium iQ), an API hub to facilitate enterprise interconnectedness, an ML-powered data platform (Tealium AudienceStream) and data management solutions.

The company recently sponsored a comprehensive economic impact study from Forrester, calculating ROI on reference customers.

Coro provides holistic cybersecurity solutions for mid-market and small to medium-sized. The platform leverages AI to identify and remediate malware, ransomware, phishing and bot security threats across all endpoints while reducing the need for a dedicated IT team. In addition, its built on the principle of non-disruptive security, allowing it to provide security solutions for organizations with limited security budgets and expertise.

This cybersecurity-as-a-service (CaaS) vendor shows how AI can support higher-level services brought to lower-level business market tiers.

As AI-powered technologies continue to advance and more organizations adopt them, IT leaders must be sure to ask themselves how the solutions they choose fit into their goals as a business. With so many vendors riding the wave of AI innovation, buyers must select their solutions carefully.

IDC predicts that AI platforms and AI application development and deployment will continue to be the fastest-growing sectors of the AI market. This list provides a starting point for organizations to evaluate the approaches and solutions that best fit their needs.

Read next:New AI software cuts development time dramatically

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10 top artificial intelligence (AI) solutions in 2022 - VentureBeat

Minerva Intelligence (TSXV:MVAI) reports the sale of a Proof-of-Concept for its AI software driver in July 2022.

DRIVER software has been issued with an annual recurring basis, giving Minerva the chance to forge, develop and maintain customer relationships alongside a reliable revenue stream. The company offers three different license tiers for end-users of varying size, as well as an upgradable limited-term Proof-of-Concept license.

A busy drilling season for mining and exploration companies coupled with a general summer slow down led to a decline in Proof-of-Concepts sold for the month of July. Changes have been made to the composition of our sales and marketing team which will reflect in an uptick of sales in the coming months. Simultaneously, we are working with large prospects to improve the functionality of DRIVER and expect to have an improved version ready for debut in September, said Scott Tillman, Minerva CEO.

Minerva Intelligence is a Vancouver, British Columbia based artificial intelligence company with an operating subsidiary office in Darmstadt, Germany. The companys software helps decision makers to better understand the earth theyre presumably going to be digging into. The companys applications primarily focus on the search for critical metals and climate risk reduction, but there are other applications its indicated for in multiple different industries.

Its likely that artificial intelligence is going to be one of the most significant trends in the tech sector in the coming years. Advances are coming hard and fast and providing substantial impact in various sectors as business try to find new ways to reduce operating costs, improve their decision making capacity, and improve upon their customer experience capabilities.

DRIVER is Minerva Intelligences AI product. Its developed for the mineral and exploration crowd with its core purpose being to help them build better 3D models and drill data to help pinpoint better drill targets, understand geometallurgical domains. DRIVER combines cloud-based processing with the companys machine learning tech to evaluate the spatial continuity present in geological numeric data.

A user puts in all the important information into the computer forming datasets and DRIVER puts together reliable 3D models from all aspects of the dataset in minutes. This helps automatically identify and catalogue potential zones of interest, which could be helpful for exploration, metallurgy, environmental protection and mining.

Since January the company has sold ten proof-of-concepts and four annual licenses. Minerva will cease to report the amount of demonstrations made during the month, and will continue to report on licenses sold on a monthly basis.

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Minerva Intelligence puts artificial intelligence in the driver's seat - Mugglehead

Mentions of artificial intelligence within the filings of companies in the maritime industry rose 67% between the first and second quarters of 2022.

In total, the frequency of sentences related to artificial intelligence between July 2021 and June 2022 was 295% higher than in 2016 when GlobalData, from whom our data for this article is taken, first began to track the key issues referred to in company filings.

When companies in the maritime industry publish annual and quarterly reports, ESG reports and other filings, GlobalData analyses the text and identifies individual sentences that relate to disruptive forces facing companies in the coming years. Artificial intelligence is one of these topics - companies that excel and invest in these areas are thought to be better prepared for the future business landscape and better equipped to survive unforeseen challenges.

To assess whether artificial intelligence is featuring more in the summaries and strategies of companies in the maritime industry, two measures were calculated. Firstly, we looked at the percentage of companies which have mentioned artificial intelligence at least once in filings during the past twelve months - this was 63% compared to 28% in 2016. Secondly, we calculated the percentage of total analysed sentences that referred to artificial intelligence.

Of the 10 biggest employers in the maritime industry, Post Italiane was the company which referred to artificial intelligence the most between July 2021 and June 2022. GlobalData identified 39 artificial intelligence-related sentences in the Italy-based company's filings - 0.3% of all sentences. Yamato mentioned artificial intelligence the second most - the issue was referred to in 0.16% of sentences in the company's filings. Other top employers with high artificial intelligence mentions included FedEx , Royal Mail and DSV .

Across all companies in the maritime industry the filing published in the second quarter of 2022 which exhibited the greatest focus on artificial intelligence came from Mainfreight . Of the document's 1,188 sentences, seven (0.6%) referred to artificial intelligence.

This analysis provides an approximate indication of which companies are focusing on artificial intelligence and how important the issue is considered within the maritime industry, but it also has limitations and should be interpreted carefully. For example, a company mentioning artificial intelligence more regularly is not necessarily proof that they are utilising new techniques or prioritising the issue, nor does it indicate whether the company's ventures into artificial intelligence have been successes or failures.

GlobalData also categorises artificial intelligence mentions by a series of subthemes. Of these subthemes, the most commonly referred to topic in the second quarter of 2022 was 'conversational platforms', which made up 36% of all artificial intelligence subtheme mentions by companies in the maritime industry.

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Filings buzz in the maritime industry: 67% increase in artificial intelligence mentions in Q2 of 2022 - Ship Technology

The need for organizations to quickly create new business models and marketing channels has accelerated AI adoption throughout the past couple of years. This is especially true in healthcare, where data analytics accelerated the development of COVID-19 vaccines. In consumer-packaged goods, Harvard Business Reviewreportedthat Frito-Lay created an e-commerce platform,Snacks.com, in just 30 days.

The pandemic also accelerated AI adoption in education, as schools were forced to enable online learning overnight. And wherever possible, the world shifted to touchless transactions, completely transforming the banking industry.

Three technology developments during the pandemic accelerated AI adoption:

[ Also readArtificial Intelligence: How to stay competitive. ]

Lets look at the pros and cons of these developments for IT leaders.

Even 60 years after Moores Law, computing power is increasing, with more powerful machines and more processing power through new chips from companies like NVidia.AI Impactsreports that computing power available per dollar has probably increased by a factor of ten roughly every four years over the last quarter of a century (measured in FLOPS or MIPS). However, the rate has been slower over the past 6-8 years.

Pros: More for less

Inexpensive computing gives IT leaders more choices, enabling them to do more with less.

Cons: Too many choices can lead to wasted time and money

Consider big data. With inexpensive computing, IT pros want to wield its power. There is a desire to start ingesting and analyzing all available data, leading to better insights, analysis, and decision-making.

But if you are not careful, you could end up with massive computing power and not enough real-life business applications.

As networking, storage, and computing costs drop, the human inclination is to use them more. But they dont necessarily deliver business value to everything.

Before the pandemic, the terms data warehouses and data lakes were standard and they remain so today. But new data architectures like data fabric and data mesh were almost non-existent. Data fabric enables AI adoption because it enables enterprises to use data to maximize their value chain by automating data discovery, governance, and consumption. Organizations can provide the right data at the right time, regardless of where it resides.

Pros: IT leaders will have the opportunity to rethink data models and data governance

It provides a chance to buck the trend toward centralized data repositories or data lakes. This might mean more edge computing and data available where it is most relevant. These advancements result in appropriate data being automatically available for decisioning critical to AI operability.

Cons: Not understanding the business need

IT leaders need to understand the business and AI aspects of new data architectures. If they dont know what each part of the business needs including the kind of data and where and how it will be used they may not create the correct type of data architecture and data consumption for proper support. ITs understanding of the business needs, and the business models that go with that data architecture, will be essential.

Statistaresearch underscores the growth of data: The total amount of data created, captured, copied, and consumed globally was 64.2 zettabytes in 2020 and is projected to reach more than 180 zettabytes in 2025. Statista research from May 2022 reports, The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic. Big data sources include media, cloud, IoT, the web, and databases.

Pros: Data is powerful

Every decision and transaction can be traced back to a data source. If IT leaders can use AIOps/MLOps to zero in on data sources for analysis and decision-making, they are empowered. Proper data can deliver instant business analysis and provide deep insights for predictive analysis.

Cons: How do you know what data to use?

More on artificial intelligence

Besieged by data from IoT, edge computing, formatted and unformatted, intelligent and unintelligible IT leaders are dealing with the 80/20 rule: What are the 20 percent credible data sources that deliver 80 percent of the business value? How do you use AI/ML ops to determine the credible data sources, and what data source should be used for analysis and decision-making? Every organization needs to find answers to these questions.

AI is becoming ubiquitous, powered by new algorithms and increasingly plentiful and inexpensive computing power. AI technology has been on an evolutionary road for more than 70 years. The pandemic did not accelerate the development of AI; it accelerated its adoption.

Harnessing AI is the challenge ahead.

[ Want best practices for AI workloads? Get theeBook: Top considerations for building a production-ready AI/ML environment. ]

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Artificial Intelligence: 3 ways the pandemic accelerated its adoption - The Enterprisers Project

DUBLIN--(BUSINESS WIRE)--The "North America Artificial Intelligence In Fintech Market Size, Share & Industry Trends Analysis Report By Component (Solutions and Services), By Deployment (On-premise and Cloud), By Application, By Country and Growth Forecast, 2022-2028" report has been added to ResearchAndMarkets.com's offering.

The North America Artificial Intelligence In Fintech Market is expected to witness market growth of 16.1% CAGR during the forecast period (2022-2028).

Artificial intelligence has enhanced customer service, which is among the most visible sectors of FinTech. In addition, artificial intelligence has advanced to the point that chatbots, virtual assistants, and artificial intelligence interfaces can effectively engage with customers. The capacity to answer basic questions has a huge impact on front-office and help-desk expenditures.

Algorithmic trading analyses data using a pre-programmed sequence of commands, enabling faster decision-making than humans. This is accomplished using machine learning, which is among the most adaptable AI technologies. In the financial industry, artificial intelligence has a lot of potential. It can assist a firm in a variety of ways, including increasing efficiency, lowering expenses, and automating procedures.

North America is among the world's largest and leading AI markets. Owing to the strong economy, the existence of major AI software and system vendors, and joint investment by organizations for the growth and development of R&D operations, the regional market has also seen the highest adoption of AI in Fintech solutions. For example, the continued economic boom in the United States, according to Baker McKenzie, has drawn significant investment in the fintech sector. Payments and insurance technology remain dominant the fintech environment in the country.

Further, the region, especially the United States, is home to a considerable proportion of the millennial population. In terms of speed and personalization, millennials clearly prefer to complete chores through digital applications and services, which fintech companies are better at supplying than banks. The region has some of the highest levels of citizen bank account penetration and the highest number of ATMs per 100,000 people. Thus, due to these factors, the growth of the regional AI in fintech market is expected to accelerate in the coming years.

The US market dominated the North America Artificial Intelligence In Fintech Market by Country in 2021, and is expected to continue to be a dominant market till 2028; thereby, achieving a market value of $7,444.2 Million by 2028. The Canada market is anticipated to grow at a CAGR of 18.7% during (2022-2028). Additionally, The Mexico market is expected to showcase a CAGR of 17.7% during (2022-2028).

Scope of the Study

Market Segments Covered in the Report:

By Component

By Deployment

By Application

By Country

Key Market Players

Key Topics Covered:

Chapter 1. Market Scope & Methodology

Chapter 2. Market Overview

Chapter 3. Competition Analysis - Global

Chapter 4. North America Artificial Intelligence In Fintech Market by Component

Chapter 5. North America Artificial Intelligence In Fintech Market by Deployment

Chapter 6. North America Artificial Intelligence In Fintech Market by Application

Chapter 7. North America Artificial Intelligence In Fintech Market by Country

Chapter 8. Company Profiles

For more information about this report visit https://www.researchandmarkets.com/r/2wsqjo

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North America Artificial Intelligence In Fintech Market Report 2022-2028: A Strong Economy, the Existence of Major AI Software and System Vendors, and...

CHICAGO, Aug. 9, 2022 /PRNewswire/ --Artificial Intelligence Marketsize is expected to grow at a Compound Annual Growth Rate (CAGR) of 36.2% during the forecast period, to reach USD 407.0 billion by 2027 from USD 86.9 billion in 2022, according to a new report by MarketsandMarkets. Since its introduction in the market, artificial intelligence technology had quickly acquired acceptance. The worldwide artificial intelligence market is expanding significantly because of the increasing demand for artificial intelligence technologies across numerous industry verticals.

Browse in-depth TOC on"Artificial Intelligence Market"447 Tables 65 Figures 431 Pages

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As per verticals, the healthcare and life sciences segment to grow at highest CAGR during the forecast period

The artificial intelligence market is segmented on verticals into BFSI, IT/ITES, Telecommunication, Healthcare and Life Sciences, Manufacturing, Retail and eCommerce, Government and Defense, Automotive Transportation and Logistics, Energy and Utilities, and other verticals, such as travel and hospitality, and education. As per verticals, the healthcare and life sciences vertical are expected to grow at the highest CAGR during the forecast period. Healthcare and life sciences, as an industry, is growing at a good pace and is expected to contribute significantly to the globally artificial intelligence market. Applications for artificial intelligence in healthcare include patient data and risk analysis, medical imaging and diagnostics, precision medicine, drug discovery, and much more. Patient data is expanding because of the widespread use of electronic medical records, and the risk analysis market is also expanding because to benefits like risk management and predictive analytics provided by AI systems to payers and healthcare providers.

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Cloud Segment to grow at the highest CAGR during the forecast period

As per deployment mode, cloud Segment to grow at the highest CAGR for the artificial intelligence market during the forecast period. The artificial intelligence market by deployment mode is segmented into cloud and on-premises. Various advantages, such as lower operational expenses, hassle-free deployment, and more, are provided by the cloud deployment approach. With increasing awareness of the advantages of cloud-based solutions, cloud adoption for Machine Learning and Natural Language Processing tools in AI is anticipated to increase. It gives businesses more operational flexibility and makes real-time analytics implementation easier for businesses.

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As per region, North America to have the highest market size during the forecasted period

As per region, North America to have the highest market size during the forecasted period. The Americas AI digital age that has grown in the retail operation is the foundation of AI in industrial developments. All sectors of retail have seen an improvement in speed, productivity, and reliability, in large part due to cutting-edge data and advanced analytical technology that help businesses make decisions based on data. Businesses are now able to compile and assess individual customer data to run promotional campaigns because of cutting-edge AI technology. For this reason, regional retailers are using cutting-edge technology to improve their e-commerce platforms.

Market Players

Some of the major Artificial Intelligence Market vendors are Google Inc. (US), Microsoft Corporation (US), NVIDIA Corporation (US), Intel Corporation (US), Samsung Electronics Co., Ltd. (South Korea), IBM Corporation (US), Amazon Web Services, Inc. (US), Oracle (US), Meta (US), Salesforce (US), Cisco (US), Siemens (US), Huawei (China), SAP SE (Germany), SAS Institute (US), Baidu, Inc. (China), Alibaba Cloud (China), iFLYTEK (China), and Hewlett Packard Enterprise Development LP (US).

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Artificial Intelligence Market Worth $407.0 Billion By 2027 - Exclusive Report by MarketsandMarkets - PR Newswire

There are teams of researchers in academia and at major AI labs these days working on the problem of AI ethics, or the moral concerns raised by AI systems. These efforts tend to be especially focused on data privacy concerns and on what is known as AI bias AI systems that, using training data with bias often built in, produce racist or sexist results, such as refusing women credit card limits theyd grant a man with identical qualifications.

There are also teams of researchers in academia and at some (though fewer) AI labs that are working on the problem of AI alignment. This is the risk that, as our AI systems become more powerful, our oversight methods and training approaches will be more and more meaningless for the task of getting them to do what we actually want. Ultimately, well have handed humanitys future over to systems with goals and priorities we dont understand and can no longer influence.

Today, that often means that AI ethicists and those in AI alignment are working on similar problems. Improving the understanding of the internal workings of todays AI systems is one approach to solving AI alignment, and is crucial for understanding when and where models are being misleading or discriminatory.

And in some ways, AI alignment is just the problem of AI bias writ (terrifyingly) large: We are assigning more societal decision-making power to systems that we dont fully understand and cant always audit, and that lawmakers dont know nearly well enough to effectively regulate.

As impressive as modern artificial intelligence can seem, right now those AI systems are, in a sense, stupid. They tend to have very narrow scope and limited computing power. To the extent they can cause harm, they mostly do so either by replicating the harms in the data sets used to train them or through deliberate misuse by bad actors.

But AI wont stay stupid forever, because lots of people are working diligently to make it as smart as possible.

Part of what makes current AI systems limited in the dangers they pose is that they dont have a good model of the world. Yet teams are working to train models that do have a good understanding of the world. The other reason current systems are limited is that they arent integrated with the levers of power in our world but other teams are trying very hard to build AI-powered drones, bombs, factories, and precision manufacturing tools.

That dynamic where were pushing ahead to make AI systems smarter and smarter, without really understanding their goals or having a good way to audit or monitor them sets us up for disaster.

And not in the distant future, but as soon as a few decades from now. Thats why its crucial to have AI ethics research focused on managing the implications of modern AI, and AI alignment research focused on preparing for powerful future systems.

So do these two groups of experts charged with making AI safe actually get along?

Hahaha, no.

These are two camps, and theyre two camps that sometimes stridently dislike each other.

From the perspective of people working on AI ethics, experts focusing on alignment are ignoring real problems we already experience today in favor of obsessing over future problems that might never come to be. Often, the alignment camp doesnt even know what problems the ethics people are working on.

Some people who work on longterm/AGI-style policy tend to ignore, minimize, or just not consider the immediate problems of AI deployment/harms, Jack Clark, co-founder of the AI safety research lab Anthropic and former policy director at OpenAI, wrote this weekend.

From the perspective of many AI alignment people, however, lots of ethics work at top AI labs is basically just glorified public relations, chiefly designed so tech companies can say theyre concerned about ethics and avoid embarrassing PR snafus but doing nothing to change the big-picture trajectory of AI development. In surveys of AI ethics experts, most say they dont expect development practices at top companies to change to prioritize moral and societal concerns.

(To be clear, many AI alignment people also direct this complaint at others in the alignment camp. Lots of people are working on making AI systems more powerful and more dangerous, with various justifications for how this helps learn how to make them safer. From a more pessimistic perspective, nearly all AI ethics, AI safety, and AI alignment work is really just work on building more powerful AIs but with better PR.)

Many AI ethics researchers, for their part, say theyd love to do more but are stymied by corporate cultures that dont take them very seriously and dont treat their work as a key technical priority, as former Google AI ethics researcher Meredith Whittaker noted in a tweet:

The AI ethics/AI alignment battle doesnt have to exist. After all, climate researchers studying the present-day effects of warming dont tend to bitterly condemn climate researchers studying long-term effects, and researchers working on projecting the worst-case scenarios dont tend to claim that anyone working on heat waves today is wasting time.

You could easily imagine a world where the AI field was similar and much healthier for it.

Why isnt that the world were in?

My instinct is that the AI infighting is related to the very limited public understanding of whats happening with artificial intelligence. When public attention and resources feel scarce, people find wrongheaded projects threatening after all, those other projects are getting engagement that comes at the expense of their own.

Lots of people even lots of AI researchers do not take concerns about the safety impacts of their work very seriously.

Sometimes leaders dismiss long-term safety concerns out of a sincere conviction that AI will be very good for the world, so the moral thing to do is to speed full ahead on development.

Sometimes its out of the conviction that AI isnt going to be transformative at all, at least not in our lifetimes, and so theres no need for all this fuss.

Sometimes, though, its out of cynicism experts know how powerful AI is likely to be, and they dont want oversight or accountability because they think theyre superior to any institution that would hold them accountable.

The public is only dimly aware that experts have serious safety concerns about advanced AI systems, and most people have no idea which projects are priorities for long-term AI alignment success, which are concerns related to AI bias, and what exactly AI ethicists do all day, anyway. Internally, AI ethics people are often siloed and isolated at the organizations where they work, and have to battle just to get their colleagues to take their work seriously.

Its these big-picture gaps with AI as a field that, in my view, drive most of the divides between short-term and long-term AI safety researchers. In a healthy field, theres plenty of room for people to work on different problems.

But in a field struggling to define itself and fearing its not positioned to achieve anything at all? Not so much.

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AI bias and AI safety teams are divided on artificial intelligence - Vox.com

The original version of this Article omitted from the author list the 9th, 10th, 11th and 12th authors Joseph Bocchini (Willis-Knighton Health System, Shreveport, LA), Soumita Das (Department of Pathology, University of California San Diego), Jane C. Burns (Department of Pediatrics, University of California San Diego and the Rady Childrens Hospital-San Diego, San Diego, CA) and Debashis Sahoo (Department of Pediatrics, University of California San Diego and the Department of Computer Science and Engineering, Jacobs School of Engineering, University of California San Diego). Additionally, the original version of this Article omitted to indicate Jane C. Burns and Debashis Sahoo as co-corresponding authors together with Pradipta Ghosh. The contact information for the corresponding authors of this Article is Jane C. Burns, M.D.; Professor, Department of Pediatrics, Director, Kawasaki Disease Research Center, University of California San Diego; 9500 Gilman Dr. MC 0641, La Jolla, CA 92093-0641 Phone: 858-246-0155: Email: jcburns@health.ucsd.edu, Debashis Sahoo, Ph.D; Associate Professor, Department of Pediatrics, University of California San Diego; 9500 Gilman Drive, MC 0703, Leichtag Building 132; La Jolla, CA 92093-0703 Phone: 858-246-1803: Fax: 858-246-0019: Email: dsahoo@ucsd.edu and Pradipta Ghosh, M.D.; Professor, Departments of Medicine, and Cell and Molecular Medicine, University of California San Diego; 9500 Gilman Drive (MC 0651), George E. Palade Bldg, Rm 232, 239; La Jolla, CA 92093. Phone: 858-822-7633: Fax: 858-822-7636: Email: prghosh@ucsd.edu. Furthermore, the list of members of the Pediatric Emergency Medicine Kawasaki Disease Research.

Group provided at the end of the Article erroneously included Joseph Bocchini, Soumita Das, Jane C. Burns and Debashis Sahoo.

Finally, the Acknowledgements section erroneously reported the grants iDASH U54HL108460 and R01HL140898 being awarded to J.C.B. and A.H.T. The correct grants awarded to J.C.B and A.H.T. are PreVAIL R61HD105590 and R01HL140898.

These errors have been corrected in both the PDF and HTML versions of the Article.

Excerpt from:
Publisher Correction: An Artificial Intelligence-guided signature reveals the shared host immune response in MIS-C and Kawasaki disease - Nature.com

North America extended its dominance for artificial intelligence (AI) hiring among apparel industry companies in the three months ending June.

The number of roles in North America made up 47.2% of total AI jobs, up from 33.2% in the same quarter last year.

That was followed by Asia-Pacific, which saw a -1.6 year-on-year percentage point change in AI roles.

The figures are compiled by GlobalData, which tracks the number of new job postings from key companies in various sectors over time. Using textual analysis, these job advertisements are then classified thematically.

GlobalData's thematic approach to sector activity seeks to group key company information by topic to see which companies are best placed to weather the disruptions coming to their industries.

These key themes, which include artificial intelligence in apparel, are chosen to cover "any issue that keeps a CEO awake at night".

By tracking them across job advertisements it allows us to see which companies are leading the way on specific issues and which are dragging their heels - and importantly where the market is expanding and contracting.

The fastest growing country was the United States, which saw 33.2% of all AI job adverts in the three months ending June 2021, increasing to 47.2% in the three months ending June this year.

That was followed by India (up 3.8 percentage points), Germany (1.3), and Belgium (0.1).

The top country for artificial intelligence roles in the apparel industry is the United States which saw 47.2% of all roles advertised in the three months ending June.

Some 12.5% of all apparel industry artificial intelligence roles were advertised in San Francisco (United States) in the three months ending June.

That was followed by York (United States) with 6.6%, Paris (France) with 5.2%, and Portland (United States) with 3.6%.

Mentions of artificial intelligence within the filings of companies in the fashion industry fell 36% between the first and second quarters of 2022.

Product Lifecycle Management and Digital Transformation Solutions for Retail, Fashion, and Apparel Companies

Cloud-Based Software for Fashion Brands, Apparel Manufacturers, and Consumer Goods Businesses (PLM, ERP, Virtual Showroom, Smart Factory, etc)

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AI roles in apparel industry on the up in North America - just-style.com

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The idea of Pepper Content germinated in a dormitory of BITS, Pilani. The story of the founders was similar to that of average Indian teenagers who wanted to pursue engineering.

The founders realized a shared passion for content. It was clear that for brands, smartphones and the Internet had changed the principles of customer engagement and experience principles. More than 700 million Internet users, businesses included, were accessing and consuming different forms of content daily. However, access to quality content was not as easy.

"We asked ourselves that if, in this instant noodle economy, items like food and medicine get ordered and delivered at the tap of a button, then why can't content be treated and delivered the same way? Every company in the world has a content need. In today's day and age, this opportunity stands at a staggering $400 billion globally. This was when we began the B2B content marketplace, Pepper Content, in 2017," said Anirudh Singla, co-founder and CEO, Pepper Content.

The co-founders with limited resources, ongoing classes, assignments, and exams, persisted in achieving their dreams. In 2017, the company received its first order of 250 articles on automotives. Pepper Content enables marketers to connect with the best writers, designers, translators, videographers, editors, and illustrators, and vets the marketplace's creative professionals using its AI algorithms to make the right match between business and creative professionals. To support its creators, Pepper Content has invested in building tools that augment their ability and make them more productive, and one of its key products Peppertype.ai is currently being used by over 200,000 users across 150 countries. The company has on-boarded over 1,000 enterprises and fast-growing startups, and works with over 2,500 customers, including organizations such as Adani Enterprises, NPS Trust, Hindustan Unilever, P&G; financial services, and insurance companies such as HDFC Bank, CRED, Groww, SBI Mutual Fund, TATA Capital, and technology firms such as Binance, Google, and Adobe.

According to the co-founders, Pepper Content is not a startup or an agency but a platform that connects people seamlessly. The company aims to create the perfect symphony between creators and brands when it comes to content. The company is enabling strategic collaboration that will have a tangible, on-ground impact.

The co-founders always wanted to take a product-first approach which meant understanding the nuances and solving for every use case. The first products were hyper-customised sheets with deep linking of formulae and scripts that enabled the company to piece together workflows. The team worked on 25,000 content pieces on Google sheets and docs in the initial stages that helped the co-founders understand the customer workflow.

Businesses can directly order quality content on the platform with faster turnaround times and complete transparency on the project's progress. The company's intelligent algorithms take care of all the management aspects: from finding the best creator-project match to running agile workflows and driving integrated tool-supported editorial checks for quality content delivery.

"The content marketing industry stands at $400 billion, globally and it is only going to scale further. However, no organised players are enabling seamless workflow for brands. Every company produces and outsources content in written, image, audio, and video formats. To date, companies are required to post requirements, bid for projects and choose from a large list of bidders, and negotiate pay, making it cumbersome and, frankly, unscalable. We are solving this by offering a managed marketplace. We take care of entire content operations, right from the ordering flow to end-to-end delivery. For companies, quality content delivery creates trust and for creators, takes care of timely payments and operational inefficiencies," said Rishabh Shekhar, co-founder and COO, Pepper Content.

The co-founders struggled in the initial days since they did not know anyone from the investor community. "We cold-emailed 80 VC and angel investors! There were a lot of questions and conversations about the company's scale and our age. It took us three months but we persisted and were oversubscribed for the seed funding round. Over the years we scaled a B2B content marketplace, built a product that was unheard of, and have credible investors backing us. We realized that age is no hindrance if your vision is clear and you have a product that creates real impact."

Originally posted here:
Artificial Intelligence Revolutionizing Content Writing - Entrepreneur