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Global Artificial Intelligence (AI) in Education Market Size study report with COVID-19 effect is considered to be an extremely knowledgeable and in-depth evaluation of the present industrial conditions along with the overall size of the Artificial Intelligence (AI) in Education industry, estimated from 2020 to 2025. The research report also provides a detailed overview of leading industry initiatives, potential market share of Artificial Intelligence (AI) in Education, and business-oriented planning, etc. The study discusses favorable factors related to current industrial conditions, levels of growth of the Artificial Intelligence (AI) in Education industry, demands, differentiable business-oriented approaches used by the manufacturers of the Artificial Intelligence (AI) in Education industry in brief about distinct tactics and futuristic prospects.

Major Players Covered in this Report are:IBM, Third Space Learning, Metacog, Jenzabar, Jellynote, Cognizant, Querium Corporation, Fishtree, Knewton, Google, DreamBox Learning, Blackboard, Nuance Communications, Cognii, Century-Tech, Osmo, Pearson, Carnegie Learning, ALEKS, Elemental Path, Quantum Adaptive Learning, Liulishuo, AWS, Microsoft, Bridge-U

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The Artificial Intelligence (AI) in Education Market study report analyses the industrys growth patterns through Past Research and forecasts potential prospects based on comprehensive analysis. The report provides extensive market share, growth, trends , and forecasts for the 20202025 period. The study offers key information on the Artificial Intelligence (AI) in Education market status, which is a valuable source of advice and guidance for companies and individuals involved in the industry.

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Market Segmentation:

The report is divided into major categories comprising product, distribution channel, application, and end users. Every segment is further sub-segmented into several sub-segmented that are deeply analyzed by experts to offer valuable information to the buyers and market players. Every segment is studied thoroughly in order to offer a better picture to the buyers and stakeholders to benefit from. Information like highest prevailing product, highly demanded product by the application segment and end users are rightly mentioned in the Artificial Intelligence (AI) in Education report.

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Regional Insights:

The Artificial Intelligence (AI) in Education market is segmented as North America, South America, Europe, Asia Pacific, and Middle East and Africa. Researchers have thoroughly studied about the historical market. With extensive research, experts have offered details on the current and the forecast demand made by these regions. The Artificial Intelligence (AI) in Education report also includes highlights on the prevailing product demanded by end users and end customers for better understanding of product demand by producers. This will help the producers and the marketing executives to plan their production quantity and plan effective marketing strategies to more buyers. Businesses can hence, increase their product portfolio and expand their global presence. Artificial Intelligence (AI) in Education market research report further offers information on the unexplored areas in these regions to help the producers to plan promotional strategies and create demand for their new and updated products. This will again help the manufacturers to increase their customers and emerge as leaders in the near future.

In this study, the years considered to estimate the market size of Artificial Intelligence (AI) in Education are as follows:

Research Objectives

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Artificial Intelligence (AI) in Education Market Advanced Technology and New Innovations by 2024 - Jewish Life News

Experts from MIT and IBM held a webinar this week to discuss where AI technologies are today and advances that will help make their usage more practical and widespread.

Image: Sompong Rattanakunchon / Getty Images

Artificial intelligence has made significant strides in recent years, but modern AI techniques remain limited, a panel of MIT professors and the director of the MIT-IBM Watson AI Lab said during a webinar this week.

Neural networks can perform specific, well-defined tasks but they struggle in real-world situations that go beyond pattern recognition and present obstacles like limited data, reliance on self-training, and answering questions like "why" and "how" versus "what," the panel said.

The future of AI depends on enabling AI systems to do something once considered impossible: Learn by demonstrating flexibility, some semblance of reasoning, and/or by transferring knowledge from one set of tasks to another, the group said.

SEE: Robotic process automation: A cheat sheet (free PDF) (TechRepublic)

The panel discussion was moderated by David Schubmehl, a research director at IDC, and it began with a question he posed asking about the current limitations of AI and machine learning.

"The striking success right now in particular, in machine learning, is in problems that require interpretation of signalsimages, speech and language," said panelist Leslie Kaelbling, a computer science and engineering professor at MIT.

For years, people have tried to solve problems like detecting faces and images and directly engineering solutions that didn't work, she said.

We have become good at engineering algorithms that take data and use that to derive a solution, she said. "That's been an amazing success." But it takes a lot of data and a lot of computation so for some problems formulations aren't available yet that would let us learn from the amount of data available, Kaelbling said.

SEE:9 super-smart problem solvers take on bias in AI, microplastics, and language lessons for chatbots(TechRepublic)

One of her areas of focus is in robotics, and it's harder to get training examples there because robots are expensive and parts break, "so we really have to be able to learn from smaller amounts of data," Kaelbling said.

Neural networks and deep learning are the "latest and greatest way to frame those sorts of problems and the successes are many," added Josh Tenenbaum, a professor of cognitive science and computation at MIT.

But when talking about general intelligence and how to get machines to understand the world there is still a huge gap, he said.

"But on the research side really exciting things are starting to happen to try to capture some steps to more general forms of intelligence [in] machines," he said. In his work, "we're seeing ways in which we can draw insights from how humans understand the world and taking small steps to put them in machines."

Although people think of AI as being synonymous with automation, it is incredibly labor intensive in a way that doesn't work for most of the problems we want to solve, noted David Cox, IBM director of the MIT-IBM Watson AI Lab.

Echoing Kaelbling, Cox said that leveraging tools today like deep learning requires huge amounts of "carefully curated, bias-balanced data," to be able to use them well. Additionally, for most problems we are trying to solve, we don't have those "giant rivers of data" to build a dam in front of to extract some value from that river, Cox said.

Today, companies are more focused on solving some type of one-off problem and even when they have big data, it's rarely curated, he said. "So most of the problems we love to solve with AIwe don't have the right tools for that."

That's because we have problems with bias and interpretability with humans using these tools and they have to understand why they are making these decisions, Cox said. "They're all barriers."

However, he said, there's enormous opportunity looking at all these different fields to chart a path forward.

That includes using deep learning, which is good for pattern recognition, to help solve difficult search problems, Tenenbaum said.To develop intelligent agents, scientists need to use all the available tools, said Kaelbling. For example, neural networks are needed for perception as well as higher level and more abstract types of reasoning to decide, for example, what to make for dinner or to decide how to disperse supplies.

"The critical thing technologically is to realize the sweet spot for each piece and figure out what it is good at and not good at. Scientists need to understand the role each piece plays," she said.

The MIT and IBM AI experts also discussed a new foundational method known as neurosymbolic AI, which is the ability to combine statistical, data-driven learning of neural networks with the powerful knowledge representation and reasoning of symbolic approaches.

Moderator Schubmehl commented that having a combination of neurosymbolic AI and deep learning "might really be the holy grail" for advancing real-world AI.

Kaelbling agreed, adding that it may be not just those two techniques but include others as well.

One of the themes that emerged from the webinar is that there is a very helpful confluence of all types of AI that are now being used, said Cox. The next evolution of very practical AI is going to be understanding the science of finding things and building a system we can reason with and grow and learn from, and determine what is going to happen. "That will be when AI hits its stride," he said.

Learn the latest news and best practices about data science, big data analytics, and artificial intelligence. Delivered Mondays

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The path to real-world artificial intelligence - TechRepublic

MONTREAL, July 14, 2020 /PRNewswire/ --Hardbacon is pleased to announce that it will receive consulting services and has obtained conditional funding of $50,000 for an artificial intelligence research and development project to predict stock prices. The grant is part of the National Research Council of Canada's Industrial Research Assistance Program (NRC IRAP).

Hardbacon, a mobile budgeting and investment tracking app, is currently developing a stock rating system, which will leverage artificial intelligence to help investors pick stocks.

Ratings generated by artificial intelligence will appear in Hardbacon's mobile application, and will also be made available under license to financial institutions wishing to use these ratings or to offer them to their customers.

"Many Hardbacon users asked us to tell them what to invest in", explained Julien Brault, CEO of Hardbacon. Until now we had refused, until one of our employees presented us with a promising academic article that he had written about the possibility of using artificial intelligence to generate predictive ratings. We are grateful that the NRC IRAP has agreed to support this project."

For more information, contact:

Julien Brault, CEO of Hardbacon; 514-250-3255; [emailprotected]

To learn more about Hardbacon, visit our website : https://hardbacon.ca/

Disclaimer:The news site hosting this press release is not associated with Hardbacon or Bacon Financial Technologies Inc. It is merely publishing a press release announcement submitted by a company, without any stated or implied endorsement of the information, product or service. Please check with a Registered Investment Adviser or Certified Financial Planner before making any investment.

About Hardbacon

Hardbacon strives to help Canadians make better financial decisions. The company, which obtained $1.1 million in funding, markets a mobile application that enables subscribers to create a plan, a budget and to analyze their investments. The mobile app, available in the App Store and Google Play, can link to bank and investment accounts for more than 100 Canadian financial institutions.

Press Contact:

Julien Brault 5142503255 https://hardbacon.ca/

SOURCE Hardbacon

https://hardbacon.ca

Read more here:
Hardbacon secures funding to develop artificial intelligence capable of predicting changes in the stock market - PRNewswire

As the insurance sector competes to win market share, Henry Jinman at EBI.AI discusses three ways companies can benefit from the power of Artificial Intelligence

The UK general insurance market continues to be fiercely competitive. While the battle for repeat business keeps downward pressure on pricing, a constantly changing regulatory agenda increases costs. Whatever the industry, successful companies know that building a business based on price alone is not sustainable. Customer service is what matters most. Its a sentiment that is reflected in the latest findings of multinational professional services company Ernst & Young (EY). It claims that non-life insurance companies in particular should invest to create innovative and satisfying end-to-end customer experiences with optimised technology that helps them become data-driven and insight-enabled in everything they do.[i]

Its time to consider the benefits of Artificial Intelligence (AI). Through its ability to capture, analyse and learn from massive amounts of data, AI should be at the centre of every enterprise serious about creating amazing customer experiences. AI tools should also support everyone, employees, managers and customers, to ask and receive the information they need, whenever and wherever they need it, quickly and using engaging, natural language.

In EBI.AIs experience, companies that introduce AI solutions such as AI assistants are rewarded with multiple benefits. By reducing the number of repetitive calls in the contact centre or customer service departments and frontline staff are better equipped to handle more complex and rewarding tasks. Meanwhile, scaling todays virtual AI solutions is easy, enabling managers to adapt to unexpected events and emergencies as they happen such as the Covid-19 pandemic. Data-driven AI solutions also make formidable weapons against the common problems facing insurance managers such as highlighting fraudulent claims and mitigating claims leakage.

Here are 3 ways AI can help the insurance industry in key areas:

1. Front-end sales train the latest AI tools to answer the most common questions quickly then maximise their ability to use critical customer data to offer personalised recommendations on policies and pricing. Integrate AI with sophisticated telematics in-car sensors or health analytics platforms to identify your most careful drivers or health-conscious clients to reward them with lower premiums so they keep coming back.

2. Product and marketing deliver customers an exceptional experience with AI tools that are welcoming, efficient and secure. Use AIs image, video and natural language capabilities to assess and analyse claims and issue fast, accurate pay-out decisions in seconds. Then build confidence and loyalty with AIs ability to flag up potential threats from scammers and hackers to keep customers sensitive details safe. Once these important foundations are in place, make AI an intrinsic part of your marketing toolkit. AI can propose personalised offerings based on customer needs and then swiftly identify opportunities for intelligent lead generation.

3. Customer management AI tools guarantee round-the-clock customer service they never sleep, go off sick or need a holiday! Virtual Customer Assistants (VCAs) for example, are a bonus to customer service departments through their ability to cross-sell, upsell and prevent agent churn. AI tools can match customers with the most qualified available agents to handle their queries or, when applied over large data sets, provide analysis of general customer sentiment over time. Maximise machine learning to add feedback functionality to insurance bots. That way, youll better understand client needs, improve services and deliver a highly personalised experience.

Dont rush in!

To make AI a success, follow a few golden rules. First of all, involve the right people in the company including budget holders, the IT department and everyday users from the very beginning. Set and manage expectations by educating your organisation about what AI can and cannot do. Be realistic when sharing timeframes for results machine-learning takes time to perfect! Also remember that AI tools thrive on good data so build a bank of reliable data that is up-to-date and above all, relevant. Finally, test AI in a real-world environment while maintaining business as usual.

Learn from real-life success stories

Follow the lead of Legal & General, General Insurance now part of LV=General Insurance, part of the Allianz Group, at the beginning of this year, EBI.AI worked with the company to create SmartHelp, an AI assistant designed to enhance the companys customer service. Since that time, nearly 11% of Legal & Generals customers use SmartHelp on the available web pages, on some of the pages usage is as high as 40% and the virtual AI assistant regularly provides over 300 answers to thousands of the most commonly asked questions.

To find out how, download the Case Study Click Here

Henry Jinman is Commercial Director at EBIAI

Established in 2014, EBI.AI is among the most advanced UK labs to create fully managed, Enterprise-grade AI Assistants. These assistants help clients to provide their customers with faster and better resolutions to their queries, and liberate front-line customer service agents from the dull, repetitive, and mundane.

EBI.AI selects the best AI and cloud services available from IBM, Amazon, Microsoft and others, combined with bespoke AI models to deliver its AI communication platform, called Lobster.

Combined with it over 19 years of experience working with big data, analytics and systems integration it has successfully implemented AI Assistants, that now handle hundreds of thousands of conversations a year across Transport & Travel, Property, Insurance, Public and Automotive industries.

For more information on EBI.AI visit their Website

Visit link:
Artificial Intelligence: 3 Benefits for the Insurance Industry - http://www.contact-centres.com

Brossard, Quebec - The Newswire - July 16, 2020 - Windfall Geotek (TSXV:WIN) is a leader in the use of Artificial Intelligence (AI) in the mining sector for digital exploration and is pleased to announce that it has started to analyze the data rich Kirkland Lake Mining camp using its CARDS Artificial Intelligence (AI) technology. The project area is approximately 932 km2 and hosts many major gold discoveries and producers.

Michel Fontaine President & CEO of Windfall Geotek states: "We are confident we can replicate the big success we had in Red Lake given the abundance and the quality of public data available in the Kirkland Lake Mining camp. Our team will use our CARDS AI tool to thoroughly examine all available assays, drill holes and mag survey data to identify high probability, high similarity targets based on the digital signature of known deposits in the area. We will then be in a great position to conclude a strategic alliance in the near future and continue to draw attention to Windfall Geotek".

Highlights of CARDS AI analysis at the Kirkland Lake area

- The Kirkland Lake Mining Camp is in Northeastern Ontario within the Abitibi Greenstone Belt and the Abitibi Gold Belt. Major structures within the camp include the Kirkland Lake Break and Cadillac Larder Lake Break which runs from Kirkland Lake, Ontario into Val d'Or Quebec, approximately 200 km.

- CARDS AI will build gold pattern signatures in one of the most prolific mining camps in Ontario.

- The project covers a total area of 932.45 km2.

- The project hosts many known gold deposits: Kirkland Lake, Kerr-Addison-Chesterville, Macassa, Young-Davidson, McBean, Upper Canada, Omega, Eastmaque, Teck-Hughes.

- Geophysical data (Mag+DEM) at 15m resolution from the Kirkland Lake-Larder Lake area survey will be utilized (GDS 1053, Ontario Geological Survey)

- Up to 4,771 gold training points originated from Ontario Drill Hole Database will be utilized (Ontario Geological Survey)

- Project will yield initial results within 6 to 8 weeks.

Click Image To View Full Size

Figure 1. Map view of the Kirkland Lake camp where CARDS AI will be used following a successful Red Lake project.

Dinesh Kandanchatha Chairman of Windfall Geotek states: "We are very pleased with the way that our CARDS AI is performing to date. With this internal project we will continue to demonstrate the power of our new business model, while building assets and value for our shareholders".

Windfall Geotek also would like to welcome Nathan Tribble onto the Board of directors today and wish Mr. Jacques Letendre all the best on his future endeavors. Mr. Tribble, P.Geo. (ON) has over 14 years of professional experience in exploration and mining, with a particular focus on gold and base metal exploration and project evaluation. His current position is Vice President Exploration at Gatling Exploration Inc and past experience includes Senior Principal Geologist for Sprott Mining, Senior Geologist for Bonterra Resources, Jerritt Canyon Gold, Kerr Mines, Northern Gold, Lake Shore Gold and Vale Inco. Mr. Tribble sits on multiple boards affiliated within the mining industry, is registered as a Professional Geoscientist in Ontario and holds a Bachelor of Science degree in Geology from Laurentian University.

About Windfall Geotek - Powered by Artificial Intelligence (AI) since 2005

Windfall Geotek is a service company using Artificial Intelligence (AI) with an extensive portfolio of shares of its clients. Windfall Geotek can count on a multidisciplinary team that includes professionals in geophysics, geology, Artificial Intelligence, and mathematics. The Company objectives is to develop a new royalty stream by significantly enhancing and participating in the exploration success rate of Mining and to continue the Land Mine detection application as a high priority.

For further information, please contact:

Michel Fontaine

President & CEO of Windfall Geotek

Telephone: 514-994-5843

Email: michel@windfallgeotek.com

Website: http://www.windfallgeotek.com

Additional information about the Company is available under the Windfall Geotek profile on SEDAR at http://www.sedar.com. Neither the TSX Venture Exchange nor does its Regulation Services Provider (as that term is defined in the policies of the TSX Venture Exchange) accept responsibility for the adequacy or accuracy of this release.

FORWARD LOOKING STATEMENTS This news release may contain forward-looking statements. Forward looking statements are statements that are not historical facts and are generally, but not always, identified by the words "expects", "plans", "anticipates", "believes", "intends", "estimates", "projects", "potential" and similar expressions, or that events or conditions "will", "would", "may", "could" or "should" occur. Although the Company believes the expectations expressed in such forward-looking statements are based on reasonable assumptions, such statements are not guarantees of future performance and actual results may differ materially from those in forward looking statements. Forward-looking statements are based on the beliefs, estimates and opinions of the Company's management on the date such statements were made. The Company expressly disclaims any intention or obligation to update or revise any forward-looking statements whether as a result of new information, future events or otherwise. Neither TSX Venture Exchange nor its Regulation Services Provider (as that term is defined in the policies of TSX Venture Exchange) accepts responsibility for the adequacy of accuracy of this release

NOT FOR DISSEMINATION IN THE UNITED STATES OR FOR DISTRIBUTION TO U.S. NEWSWIRE SERVICES AND DOES NOT CONSTITUTE AN OFFER OF THE SECURITIES DESCRIBED HEREIN

Link:
Windfall Geotek to Initiate CARDS Artificial Intelligence analysis within the Kirkland Lake Mining camp to Generate New Gold Targets - TheNewswire.ca