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PALM BEACH, Fla., June 22, 2022 /PRNewswire/ --FinancialNewsMedia.com News Commentary - Global artificial intelligence in the education sector market revenue is expected to increase significantly during the next several years due to increasing demand for real-time progress monitoring of learners/students, and efficient analysis solutions in the education and corporate learning industry. Increasing demand for unique and interactive virtual learning courses is expected to further fuel global artificial intelligence in the education sector market growth going ahead. Rising need for better-customized learning experience is further projected to augment growth of the global artificial intelligence in the education sector market. Increasing demand for Artificial Intelligence (AI) technology to simplify institutions' administrative activities is expected to propel growth of the global artificial intelligence in the education sector market in the coming years. A report from Emergen Research projected that the global artificial intelligence in the education sector market is expected to reach USD 17.83 Billion by 2027, and register a CAGR of 43.8% during the forecast period. The report said: "In terms of revenue, the on-premises segment is projected to reach a market size of 12.73 USD Billion by 2027. On the basis of deployment, the global artificial intelligence in the education sector market is segmented into cloud-based and on-premises. On-premises segment is expected to account for the largest market share in the global artificial intelligence in the education sector market during the forecast period due to rising adoption of on-premise based AI solutions in universities and educational institutions to reduce cyber-attacks and data leakage. Cloud-based segment is expected to register steady growth in terms of revenue during the forecast period owing to significantly high application of virtual assistance and cost-effective cloud-hosted learning management systems in educational institutes globally." Active Tech Companies in the markets today include: Amesite Inc. (NASDAQ: AMST), Microsoft Corporation (NASDAQ: MSFT), 2U, Inc. (NASDAQ: TWOU), Blackbaud(NASDAQ: BLKB),PowerSchool (NYSE: PWSC).

Emergen Research continued: "By end-use, the higher education segment is projected to expand at a high CAGR of 43.9% during the forecast period. On the basis of end-use, the global artificial intelligence in the education sector market is segmented into K-12 education, higher education, and corporate learning. Higher education segment is expected to account for the largest market share during the forecast period due to increasing adoption of AI in colleges and universities to improve the admission process. Colleges and universities can offer customized experiences for students by automating various processes related to administration during admissions. AI can be used to assist with immigration processes, student accommodation allocation, and course registration, among others. Corporate learning segment is expected to register significant growth in terms of revenue during the forecast period due to increasing demand to reduce training gap in corporate learnings; artificial intelligence enables instructors to track and evaluate trainee's progress continually. Besides, AI can offer unique learning methods like game-based courses, which is expected to further augment revenue growth of this segment during the forecast period."

Amesite Inc.(NASDAQ: AMST) BREAKING NEWS: Amesite Launches V5 Platform, Enabling Every Business, University and Museum to Deliver or Sell AI-Powered, Branded eLearning - Amesite Inc., a leading artificial intelligence software company offering a cloud-based learning platform and content creation services for business, university, non-profit, and government agency learning and upskilling, announces the expansion of its capabilities to serve larger entities, with the launch of Version 5.0 of its AI-driven online learning platform.

"According to the Department of Labor, there are more than 65,000 medium and large companies with over 250 employees in the U.S. Our platform is now an out-of-the-box solution for the enterprises that are onboarding, training, and upskilling large numbers of workers," commented Dr. Ann Marie Sastry, founder, and CEO of Amesite. "Our larger customers have told us their needs and we listened and now we're delighted to be able to offer our proven, advanced features to the biggest markets in ed-tech."

Version 5.0 of the Amesite Learning Community EnvironmentSM (LCE SM) delivers features and service attributes that offer scalability, security, flexibility and the ability to sell learning products:

The company aims to serve the incredible need for U.S. and Global upskilling. According to Statista, the U.S. workplace training industry was valued at approximately $165 billion in 2020. Global Market Insights expects eLearning revenue to reach $1T by 2028. CONTINUED Read this full release for Amesite at: https://ir.amesite.com/

Other recent developments in the tech industry include:

Microsoft Corporation (NASDAQ: MSFT)was recently joined byTorontoMayorJohn Toryto celebrate the official opening of its new Canadian headquarters and significant investments the company has made over the past four years acrossCanada. This news also coincides with the launch of new research from EY about Microsoft's impact on the Canadian economy.

"Microsoft has been deeply rooted inCanadafor nearly 40 years and our commitment to help growCanada'sinnovation economy has never been stronger," saidKevin Peesker, President of Microsoft Canada. "With the launch of our new headquarters, official opening of our Data Innovation Centre of Excellence and expansion of our regional presence, even more organizations of all sizes and sectors can leverage the power of cloud and data to accelerate their organization's growth and drive new economic opportunity forCanada."

edX,a leading global online learning platform from 2U, Inc. (NASDAQ: TWOU), and theUniversity of Maryland'sA.James Clark Schoolof Engineering ranked twelfth in the country in online engineering programs recently announced the launch of a newMaster of Professional Studies (MPS) in Product Management. This new degree from UMD, launched in partnership with edX, is one of the first fully-online product management graduate degrees available from an accredited non-profit college or university, and is offered at approximately$25,000.

Product management named a top 10 'Best Job in America for 2022' byGlassdoor is a growing in-demand profession, with24 percent annual growth in job openingsandhigh earning potential. Following the success of itsProduct Management Professional Certificateprogram on edX, which has enrolled over 60,000 learners since it started inMay 2020, UMD decided to develop and launch this competitively priced degree program with edX.

Blackbaud(NASDAQ: BLKB), the world's leading cloud software company powering social good, recentlyannounced the launch of Prospect Insightsa new software tool within Blackbaud Raiser's Edge NXTthat enables social good professionals to access actionable, AI-powered insights to drive more major giving.

"Intelligent software is a necessary component of modern fundraising," saidCarrie Cobb, chief data officer, Blackbaud. "Social good organizations rely on major gifts, yet many have limited resources on staff to mine through multiple data sources and identify potential donors. Prospect Insights elivers that intelligence, inside of Raiser's Edge NXT, to automate, simplify and improve the process and outcomes for the organization."

PowerSchool (NYSE: PWSC), the leading provider of cloud-based software for K-12 education in North America, recently announced theSchool District of Newberry County(SDNC) in South Carolina has expanded their use of PowerSchool solutions to make more data-informed educational decisions. SDNC recently selectedPowerSchool's Unified ClassroomPerformance Mattersas its student assessment software solution after years of benefiting fromPowerSchool Student Information System(SIS),PowerSchool's Unified ClassroomSchoology Learning, andPowerSchool Enrollment. Upon completion of implementation, SDNC plans to leverage their PowerSchool solutions to offer more personalized and data-driven instruction to its students.

DISCLAIMER: FN Media Group LLC (FNM), which owns and operates FinancialNewsMedia.com and MarketNewsUpdates.com, is a third party publisher and news dissemination service provider, which disseminates electronic information through multiple online media channels. FNM is NOT affiliated in any manner with any company mentioned herein. FNM and its affiliated companies are a news dissemination solutions provider and are NOT a registered broker/dealer/analyst/adviser, holds no investment licenses and may NOT sell, offer to sell or offer to buy any security. FNM's market updates, news alerts and corporate profiles are NOT a solicitation or recommendation to buy, sell or hold securities. The material in this release is intended to be strictly informational and is NEVER to be construed or interpreted as research material. All readers are strongly urged to perform research and due diligence on their own and consult a licensed financial professional before considering any level of investing in stocks. All material included herein is republished content and details which were previously disseminated by the companies mentioned in this release. FNM is not liable for any investment decisions by its readers or subscribers. Investors are cautioned that they may lose all or a portion of their investment when investing in stocks. For current services performed FNM has been compensated twenty six hundred dollars for news coverage of the current press releases issued by Amesite Inc. by a non-affiliated third party. FNM HOLDS NO SHARES OF ANY COMPANY NAMED IN THIS RELEASE.

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Global Artificial Intelligence In The Education Sector Expected To Reach $17 Billion By 2027 - PR Newswire

(The Conversation is an independent and nonprofit source of news, analysis and commentary from academic experts.)

(THE CONVERSATION) Much as robots have transformed entire swaths of the manufacturing economy, artificial intelligence and automation are now changing information work, letting humans offload cognitive labor to computers. In journalism, for instance, data mining systems alert reporters to potential news stories, while newsbots offer new ways for audiences to explore information. Automated writing systems generate financial, sports and elections coverage.

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A common question as these intelligent technologies infiltrate various industries is how work and labor will be affected. In this case, who or what will do journalism in this AI-enhanced and automated world, and how will they do it?

The evidence I assembled in my 2019 book Automating the News: How Algorithms are Rewriting the Media suggests that the future of AI-enabled journalism will still have plenty of people around. However, the jobs, roles and tasks of those people will evolve and look a bit different. Human work will be hybridized blended together with algorithms to suit AIs capabilities and accommodate its limitations.

Augmenting, not substituting

Some estimates suggest that current levels of AI technology could automate only about 15% of a reporters job and 9% of an editors job. Humans still have an edge over non-Hollywood AI in several key areas that are essential to journalism, including complex communication, expert thinking, adaptability and creativity.

Reporting, listening, responding and pushing back, negotiating with sources, and then having the creativity to put it together AI can do none of these indispensable journalistic tasks. It can often augment human work, though, to help people work faster or with improved quality. And it can create new opportunities for deepening news coverage and making it more personalized for an individual reader or viewer.

Newsroom work has always adapted to waves of new technology, including photography, telephones, computers or even just the copy machine. Journalists will adapt to work with AI, too. As a technology, it is already and will continue to change newswork, often complementing but rarely substituting for a trained journalist.

Ive found that more often than not, AI technologies appear to actually be creating new types of work in journalism.

Take for instance the Associated Press, which in 2017 introduced the use of computer vision AI techniques to label the thousands of news photos it handles every day. The system can tag photos with information about what or who is in an image, its photographic style, and whether an image is depicting graphic violence.

The system gives photo editors more time to think about what they should publish and frees them from spending lots of time just labeling what they have. But developing it took a ton of work, both editorial and technical: Editors had to figure out what to tag and whether the algorithms were up to the task, then develop new test data sets to evaluate performance. When all that was done, they still had to supervise the system, manually approving the suggested tags for each image to ensure high accuracy.

Stuart Myles, the AP executive who oversees the project, told me it took about 36 person-months of work, spread over a couple of years and more than a dozen editorial, technical and administrative staff. About a third of the work, he told me, involved journalistic expertise and judgment that is especially hard to automate. While some of the human supervision may be reduced in the future, he thinks that people will still need to do ongoing editorial work as the system evolves and expands.

Semi-automated content production

In the United Kingdom, the RADAR project semi-automatically pumps out around 8,000 localized news articles per month. The system relies on a stable of six journalists who find government data sets tabulated by geographic area, identify interesting and newsworthy angles, and then develop those ideas into data-driven templates. The templates encode how to automatically tailor bits of the text to the geographic locations identified in the data. For instance, a story could talk about aging populations across Britain, and show readers in Luton how their community is changing, with different localized statistics for Bristol. The stories then go out by wire service to local media who choose which to publish.

The approach marries journalists and automation into an effective and productive process. The journalists use their expertise and communication skills to lay out options for storylines the data might follow. They also talk to sources to gather national context, and write the template. The automation then acts as a production assistant, adapting the text for different locations.

RADAR journalists use a tool called Arria Studio, which offers a glimpse of what writing automated content looks like in practice. Its really just a more complex interface for word processing. The author writes fragments of text controlled by data-driven if-then-else rules. For instance, in an earthquake report you might want a different adjective to talk about a quake that is magnitude 8 than one that is magnitude 3. So youd have a rule like, IF magnitude > 7 THEN text = strong earthquake, ELSE IF magnitude < 4 THEN text = minor earthquake. Tools like Arria also contain linguistic functionality to automatically conjugate verbs or decline nouns, making it easier to work with bits of text that need to change based on data.

Authoring interfaces like Arria allow people to do what theyre good at: logically structuring compelling storylines and crafting creative, nonrepetitive text. But they also require some new ways of thinking about writing. For instance, template writers need to approach a story with an understanding of what the available data could say to imagine how the data could give rise to different angles and stories, and delineate the logic to drive those variations.

Supervision, management or what journalists might call editing of automated content systems are also increasingly occupying people in the newsroom. Maintaining quality and accuracy is of the utmost concern in journalism.

RADAR has developed a three-stage quality assurance process. First, a journalist will read a sample of all of the articles produced. Then another journalist traces claims in the story back to their original data source. As a third check, an editor will go through the logic of the template to try to spot any errors or omissions. Its almost like the work a team of software engineers might do in debugging a script and its all work humans must do, to ensure the automation is doing its job accurately.

Developing human resources

Initiatives like those at the Associated Press and at RADAR demonstrate that AI and automation are far from destroying jobs in journalism. Theyre creating new work as well as changing existing jobs. The journalists of tomorrow will need to be trained to design, update, tweak, validate, correct, supervise and generally maintain these systems. Many may need skills for working with data and formal logical thinking to act on that data. Fluency with the basics of computer programming wouldnt hurt either.

As these new jobs evolve, it will be important to ensure theyre good jobs that people dont just become cogs in a much larger machine process. Managers and designers of this new hybrid labor will need to consider the human concerns of autonomy, effectiveness and usability. But Im optimistic that focusing on the human experience in these systems will allow journalists to flourish, and society to reap the rewards of speed, breadth of coverage and increased quality that AI and automation can offer.

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Artificial intelligence-enhanced journalism offers a glimpse of the future of the knowledge economy - Roanoke Times

Mentions of artificial intelligence within the filings of companies in the mining industry fell 49% between the third and fourth quarters of 2021.

In total, the frequency of sentences related to artificial intelligence during 2021 was 161% higher than in 2016 when GlobalData, from which our data for this article is taken, first began to track the key issues referred to in company filings.

When companies in the mining 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 mining industry, two measures were calculated. Firstly, we looked at the percentage of companies that have mentioned artificial intelligence at least once in filings during the past 12 months this was 45%, compared to 22% in 2016. Secondly, we calculated the percentage of total analysed sentences that referred to artificial intelligence.

Of the 10 biggest employers in the mining industry, Nippon Steel was the company that referred to artificial intelligence the most during 2021. GlobalData identified 18 artificial intelligence-related sentences in the Japan-based company's filings 0.3% of all sentences. ThyssenKrupp mentioned artificial intelligence the second most the issue was referred to in 0.11% of sentences in the company's filings. Other top employers with high artificial intelligence mentions included Honeywell, CIL, and Sibanye-Stillwater.

This analysis provides an approximate indication of which companies are focusing on artificial intelligence and how important the issue is considered within the mining 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 fourth quarter of 2021 was 'smart robots', which made up 67% of all artificial intelligence subtheme mentions by companies in the mining industry.

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Filings buzz in the mining industry: 49% decrease in artificial intelligence mentions in Q4 of 2021 - Mining Technology

What is AI? Artificial intelligence (AI) is the ability of a machine or computer to imitate the capabilities of the human mind. AI taps into multiple technologies to equip machines in planning, acting, comprehending, learning, and sensing with human-like intelligence. AI systems may perceive environments, recognize objects, make decisions, solve problems, learn from experience, and imitate examples. These abilities are combined to accomplish actions that would otherwise require humans to do, such as driving a car or greeting a guest.

Artificial intelligence may have entered everyday conversation over the last decade or so but it has been around much longer (see the History of AI section below). The relatively recent rise in its prominence is not by accident.

AI technology, and especially machine learning, relies on the availability of vast volumes of information. The proliferation of the Internet, the expansion of cloud computing, the rise of smartphones, and the growth of the Internet of Things has created enormous quantities of data that grows every day. This treasure trove of information combined with the huge gains made in computing power have made the rapid and accurate processing of enormous data possible.

Today, AI is completing our chat conversations, suggesting email responses, providing driving directions, recommending the next movie we should stream, vacuuming our floors, and performing complex medical image analyses.

The history of artificial intelligence goes as far back as ancient Greece. However, its the rise of electronic computing that made AI a real possibility. Note that what is considered AI has changed as the technology evolves. For example, a few decades ago, machines that could perform optimal character recognition (OCR) or simple arithmetic were categorized as AI. Today, OCR and basic calculations are not considered AI but rather an elementary function of a computer system.

Artificial intelligence asserts that there are principles governing the actions of intelligent systems. It is based on reverse-engineering human capabilities and traits onto a machine. The system uses computational power to exceed what the average human is capable of doing. The machine must learn to respond to certain actions. It relies on historical data and algorithms to create a propensity model. Machines learn from experience to perform cognitive tasks that are ordinarily the preserve of the human brain. The system automatically learns from features or patterns in the data.

AI is founded on two pillars engineering and cognitive science. The engineering involves building the tools that rely on human-comparable intelligence. Large volumes of data are combined with series of instructions (algorithms) and rapid iterative processing. Cognitive science involves emulating how the human brain works, and brings to AI multiple fields including machine learning, deep learning, neural networks, cognitive computing, computer vision, natural language processing, and knowledge reasoning.

Artificial intelligence isnt one type of system. Its a diverse domain. Theres the simple, low-level AI systems focused on performing a specific task such as weather apps, business data analysis apps, taxi hailing apps, and digital assistants. This is the type of AI, called "Narrow AI", that the average person is most likely to interact with. Their main purpose is driving efficiency.

On the other end of the spectrum are advanced systems that emulate human intelligence at a more general level and can tackle complex tasks. These include thinking creatively, abstractly, and strategically. Strictly speaking, this kind of truly sentient machine, called "Artificial General Intelligence" or AGI, only exists on the silver screen for now, though the race toward its realization is accelerating.

Humans have pursued artificial intelligence in recognition of how invaluable it can be for business innovation and digital transformation. AI can cut costs and introduce levels of speed, scalability, and consistency that is otherwise out of reach. You probably interact with some form of AI multiple times each day. The applications of AI are too numerous to exhaustively cover here. Heres a high level look at some of the most significant ones.

As cyberattacks grow in scale, sophistication, and frequency, human-dependent cyber defenses are no longer adequate. Traditionally, anti-malware applications were built with specific threats in mind. Virus signatures would be updated as new malware was identified.

But keeping up with the sheer number and diversity of threats eventually becomes a near impossible task. This approach was reactive and depended on the identification of a specific malware for it to be added to the next update.

AI-based anti-spam, firewall, intrusion detection/prevention, and other cybersecurity systems go beyond the archaic rule-based strategy. Real-time threat identification, analysis, mitigation, and prevention is the name of the game. They deploy AI systems that detect malware traits and take remedial action even without the formal identification of the threat.

AI cybersecurity systems rely on the continuous feed of data to recognize patterns and backtrack attacks. By feeding algorithms large volumes of information, these systems learn how to detect anomalies, monitor behavior, respond to threats, adapt to attack, and issue alerts.

Also referred to as speech-to-text (STT), speech recognition is technology that recognizes speech and converts it into digital text. Its at the heart of computer dictation apps, as well as voice-enabled GPS and voice-driven call answering menus.

Natural language processing (NLP) relies on a software application to decipher, interpret, and generate human-readable text. NLP is the technology behind Alexa, Siri, chatbots, and other forms of text-based assistants. Some NLP systems use sentiment analysis to make out the attitude, mood, and subjective qualities in a language.

Also known as machine vision or computer vision, image recognition is artificial intelligence that allows one to classify and identify people, objects, text, actions, and writing occurring within moving or still images. Usually powered by deep neural networks, image recognition has found application in self-driving cars, medical image/video analysis, fingerprint identification systems, check deposit apps, and more.

E-commerce and entertainment websites/apps leverage neural networks to recommend products and media that will appeal to the customer based on their past activity, the activity of similar customers, the season, the weather, the time of day, and more. These real-time recommendations are customized to each user. For e-commerce sites, recommendations not only grow sales but also help optimize inventory, logistics, and store layout.

The stock market can be extremely volatile in times of crisis. Billions of dollars in market value may be wiped out in seconds. An investor who was in a highly profitable position one minute could find themselves deep in the red shortly thereafter. Yet, its near impossible for a human to react quick enough to market-influencing events. High-frequency trading (HFT) systems are AI-driven platforms that make thousands or millions of automated trades per day to maintain stock portfolio optimization for large institutions.

Lyft, Uber, and other ride-share apps use AI to connect requesting riders to available drivers. AI technology minimizes detours and wait times, provides realistic ETAs, and deploys surge-pricing during spikes in demand.

Self-driving cars are not yet standard in most of the world but theres already been a concerted push to embed AI-based safety functions to detect dangerous scenarios and prevent accidents.

Unlike land-based vehicles, the margin for error in aircraft is extremely narrow. Given the altitude, a small miscalculation may lead to hundreds of fatalities. Aircraft manufacturers had to push safety systems and become one of the earliest adopters of artificial intelligence.

To minimize the likelihood and impact of human error, autopilot systems have been flying military and commercial aircraft for decades. They use a combination of GPS technology, sensors, robotics, image recognition, and collision avoidance to navigate planes safely through the sky while keeping pilots and ground crew updated as needed.

Artificial Intelligence accelerates and simplifies test creation, execution, and maintenance through AI-powered intelligent test automation. AI-based machine learning and advanced optical character recognition (OCR) provide for advanced object recognition, and when combined with AI-based mockup identification, AI-based recording, AI-based text matching, and image-based automation, teams can reduce test creation time and test maintenance efforts,and boost test coverage and resilience of testing assets.

Artificial intelligence allows you to test earlier and faster with functional testing solutions. Combine extensive technology support with AI-driven capabilities. Deliver the speed and resiliency that supports rapid application changes within a continuous delivery pipeline.

Both IT and business face the challenges of too many manual, error-prone workflows, an ever-increasing volume of requests, employees dissatisfied with the level and quality of service, and more. Artificial Intelligence and machine learning technology can take service management to the next level:

Read How AI Is Enabling Enterprise Service Management from the resource list below for more thoughts and information on the role of artificial intelligence (AI) in the adoption and expansion of enterprise service management (ESM).

What is true of IT support, is also true for ESM; AI makes operations and outcomes better. To find out more read Ten Tips for Empowering Your IT Support with AI.

Robotic process automation (RPA) uses software robots that mimic screen-based human actions to perform repetitive tasks and extend automation to interfaces with difficult or no application programming interfaces (APIs). Thats why RPA is perfect for automating processes typically completed by humans or that require human intervention. Resilient robots adapt to screen changes and keep processes flowing when change happens. When powered by AI-based machine learning, RPA robots identify screen objects even ones they havent seen before and emulate human intuition to determine their functions. They use OCR to read text (for example, text boxes and links) and computer vision to read visual elements (for example, shopping cart icons and login buttons). When a screen object changes, robots adapt. Machine learning drives them to continuously improve how they see and interact with screen objects just like a human would.

There are plenty of ways you could leverage artificial intelligence for your business to stay competitive, drive growth, and unlock value. Nevertheless, your organization doesnt possess infinite resources. You must prioritize. Begin by defining what your organizations values and strategic objectives are. From that point, assess the possible applications of AI against these values and objectives. Choose the AI technology that is bound to deliver the biggest impact for the business.

The world is only going to grow more AI-dependent. Its no longer about whether to adopt AI but when. Organizations that tap into AI ahead of their peers could gain a significant competitive advantage. Developing and pursuing a well-defined AI strategy is where it all begins. It may take a bit of experimenting before you know what will work for you.

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What Is Artificial Intelligence (AI)? | Micro Focus

Artificial intelligence (AI) has taken on many new shapes and use cases as experts learn more about whats possible with big data and smart algorithms.

Todays AI market, then, consists of a mixture of tried-and-true smart technologies with new optimizations and advanced AI that is slowly transforming the way we do work and live daily life.

Read on to learn about some artificial intelligence trends that are making experts most excited for the future of AI:

More on the AI market: Artificial Intelligence Market

With its ability to follow basic tasks and routines based on smart programming and algorithms, artificial intelligence is becoming embedded in the way organizations automate their business processes.

AIOps and MLops are common use cases for AI and automation, but the breadth and depth of what AI can automate in the enterprise is quickly growing.

Bali D.R., SVP at Infosys, a global digital services and consulting firm, believes that AI is moving toward a certain level of hyper-automation, partially in response to the unexpected changes in manual data and procedures caused by the pandemic.

We are in the second inflection point for AI as it graduates from consumer AI, towards enterprise-grade AI, D.R. said. Being exposed to an over-reliance on manual procedures, such as mass rescheduling in the airline industry, unprecedented loan applications in banks, etc., the industries are now turning to hyper-automation that combines robotic process automation with modern machine learning to ensure they can better handle surges in the future.

Although AI automation is still mostly limited to interval and task-oriented automation that requires little imagination or guesswork on the part of the tool, some experts believe we are moving closer to more applications for intelligent automation.

David Tareen, director for artificial intelligence at SAS, a top analytics and AI software company, had this to say about the future of intelligent automation:

Intelligent automation is an area I expect to grow, Tareen said. Just like we automated manufacturing work, we will use AI heavily to automate knowledge work.

The complexity comes in because knowledge work has a high degree of variability. For example, an organization will receive feedback on their products or services in different ways and often in different languages as well. AI will need to ingest, understand, and modify processes in real-time before we can automate knowledge work at large.

AI, automation, and the job market: Artificial Intelligence and Automation

Because of the depth of big data and AIs reliance on it, theres always the possibility that unethical or ill-prepared data will make it into an AI training data set or model.

As more companies recognize the importance of creating AI that conducts its operations in a compliant and ethical manner, a number of AI developers and service providers are starting to offer responsible AI solutions to their customers.

Read Maloney, SVP of marketing at H2O.ai, a top AI and hybrid cloud company, explained what exactly responsible AI is and some of the different initiatives that companies are undertaking to improve their AI ethics.

AI creates incredible new opportunities to improve the lives of people around the world, Maloney said. We take the responsibility to mitigate risks as core to our work, so building fairness, interpretability, security, and privacy into our AI solutions is key.

Maloney said the market is seeing an increased adoption of the core pillars of responsible AI, which he shared with Datamation:

Companies are exploring several ways to make their AI more responsible, and most are starting with cleaning and assessing both data sets and existing AI models.

Brian Gilmore, director of IoT product management at InfluxData, a database solutions company, believes that one of the top options for model and data set management is distributed ledger technology (DLT).

As attention builds around the ethical and cultural impact of AI, some organizations are beginning to invest in ancillary but important technologies that utilize consensus and other trust-ensuring systems as a part of the AI framework, Gilmore said. For example, distributed ledger technology provides a sidecar platform for auditable proof of integrity for models and training data.

The decentralized ownership, distribution of access, and shared accountability of DLT can bring significant transparency to AI development and application across the board. The dilemma is whether for-profit corporations are willing to participate in a community model, trading transparency for consumer trust in something as mission critical as AI.

See more: The Ethics of Artificial Intelligence (AI)

Up to this point, AI has most frequently been used to optimize business processes and automate some home routines for consumers.

However, some experts are beginning to realize the potential that AI-powered models can have for solving global issues.

Read Maloney at H2O.ai has worked with people from a variety of industries to brainstorm how AI can be used for the greater good.

We work with many like-minded customers, partners, and organizations tackling issues from education, conservation, health care, and more, Maloney said. AI for good is fundamental to not only our work, including current work on climate change, wildfires, and hurricane predictions, but we are seeing more and more AI for good work to make the world a better place across the AI industry.

Some of the most exciting applications of altruistic AI are being implemented in early education right now.

For instance, Helen Thomas, CEO ofDMAI, an AI-powered health care and education company, offers an AI-powered product to ensure that preschool-aged children are getting the education they need, despite potential pandemic setbacks:

On top of pre-existing barriers to preschool education, including cost and access, recent research findings suggest children born during the COVID-19 pandemic display lower IQ scores than those born before January 2020, which means toddlers are less prepared for school than ever before.

DMAI DBA Animal Island Learning Adventure (AILA) is changing this with AI. [Our product] harnesses cognitive AI to deliver appropriate lessons in a consistent and repetitious format, supportive of natural learning patterns

Recognizing learning patterns that parents might miss, the AI creates an adaptive learning journey and doesnt allow the child to move forward until theyve mastered the skills and concepts presented. This intentional delivery also increases attention span over time, ensuring children step into the classroom with the social-emotional intelligence to succeed.

More on this topic: How AI is Being Used in Education

Internet of Things (IoT) devices have become incredibly widespread among both enterprise and personal users, but what many tech companies still struggle with is how to gather actionable insights from the constant inflow of data from these devices.

AIoT, or the idea of combining artificial intelligence with IoT products, is one field that is starting to address these pools of unused data, giving AI the power to translate that data quickly and intelligently.

Bill Scudder, SVP and AIoT general manager at AspenTech, an industrial AI solutions company, believes that AIoT is one of the most crucial fields for enabling more intelligent, real-time business decisions.

Forrester has noted that up to 73% of all data collected within the enterprise goes unused, which highlights a critical challenge with IoT, Scudder said. As the volume of connected devices for example, in industrial IoT settings continues to increase, so does the volume of data collected from these devices.

This has resulted in a trend seen across many industries: the need to marry AI and IoT. And heres why: where IoT allows connected devices to create and transmit data from various sources, AI can take that data one step further, translating data into actionable insights to fuel faster, more intelligent business decisions. This is giving way to the rising trend of artificial intelligence of things or AIoT.

Decision intelligence (DI) is one of the newest artificial intelligence concepts that takes many current business optimizations a step farther, by using AI models to analyze wide-ranging sets of commercial data. These analyses are used to predict future outcomes for everything from products to customers to supply chains.

Sorcha Gilroy, data science team lead at Peak, a commercial AI solutions provider, explained that although decision intelligence is a fairly new concept, its already gaining traction with larger enterprises because of its detailed business intelligence (BI) offerings.

Decision intelligence is a new category of software that facilitates the commercial application of artificial intelligence, providing predictive insight and recommended actions to users, Gilroy said. It is outcome focused, meaning a solution must deliver against a business need before it can be classed as DI.

Recognized by Gartner and IDC, it has the potential to be the biggest software category in the world and is already being utilized by businesses across a variety of use cases, from personalizing shopper experiences to streamlining complex supply chains. Brands such as Nike, PepsiCo, and ASOS are known to be using DI already.

Read next: Top Performing Artificial Intelligence Companies

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Artificial Intelligence Trends & Predictions for 2022 | Datamation