AI News, BOOK REVIEW: SpringerOpen blog artificial intelligence
- On 27. januar 2019
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19 Artificial Intelligence Technologies To Look For In 2019
Tech decision makersare (and should keep)looking for ways toeffectivelyimplement artificial intelligence into their businesses and, therefore, drive value.And though all AI technologies most definitely have their own merits,not allof themare worth investing in.
By providing algorithms, APIs (application programming interface), development and training tools, big data, applications and other machines, ML platforms are gaining more and more traction every day.
The last one is actuallythe first and only audience management tool in the world that applies real AI and machine learning to digital advertising to find the most profitable audience or demographic group for any ad.
And if you haven’t seen them already, expect the imminent appearance and wide acceptance of AI-optimized silicon chips that can be inserted right into your portable devices and elsewhere.
Deep learning platforms use a unique form of ML that involves artificial neural circuits with various abstraction layers that can mimic the human brain, processing data and creating patterns for decision making.
It allows for more natural interactions between humans and machines, including interactions related to touch, image, speech and body language recognition, and is big within the market research field.
It’s a solution that lets you make the most of your human talent and move employees into more strategic and creative positions, so their actions can really make an impact on the company's growth.
Their digital twins are mainly lines of software code, but the most elaborate versions look like 3-D computer-aided design drawings full of interactive charts, diagrams, and data points.
AI and ML are now being used to move cyberdefense into a new evolutionary phase in response to an increasingly hostile environment: Breach Level Index detected a total of over 2 billion breached records during 2017.
Recurrent neural networks, which are capable of processing sequences of inputs, can be used in combination with ML techniques to create supervised learning technologies, which uncover suspicious user activity and detect up to 85% of all cyber attacks.
Startups such as Darktrace, which pairs behavioral analytics with advanced mathematics to automatically detect abnormal behavior within organizations and Cylance, which applies AI algorithms to stop malware and mitigate damage from zero-day attacks, are both working in the area of AI-powered cyber defense.
Compliance is the certification or confirmation that a person or organization meets the requirements of accepted practices, legislation, rules and regulations, standards or the terms of a contract, and there is a significant industry that upholds it.
And the volume of transaction activities flagged as potential examples of money laundering can be reduced as deep learning is used to apply increasingly sophisticated business rules to each one.
Merlon Intelligence, a global compliance technology company that supports the financial services industry to combat financial crimes, and Socure, whose patented predictive analytics platform boosts customer acceptance rates while reducing fraud and manual reviews.
While some are rightfully concerned about AI replacing people in the workplace, let’s not forget that AI technology also has the potential to vastly help employees in their work, especially those in knowledge work.
Content creation now includes any material people contribute to the online world, such as videos, ads, blog posts, white papers, infographics and other visual or written assets.
Nano Vision, a startup that rewards users with cryptocurrency for their molecular data, aims to change the way we approach threats to human health, such as superbugs, infectious diseases, and cancer, among others.
Another player utilizing peer-to-peer networks and AI is Presearch, a decentralized search engine that’s powered by the community and rewards members with tokens for a more transparent search system.
And Affectiva’s Emotion AI is used in the gaming, automotive, robotics, education, healthcare industries, and other fields, to apply facial coding and emotion analytics from face and voice data.
It uses software to automate customer segmentation, customer data integration, and campaign management, and streamlines repetitive tasks, allowing strategic minds to get back to doing what they do best.
The software automates all the process of campaign management and optimization, making more than 480 daily adjustments per ad to super-optimize campaigns and managing budgets across multiple platforms and over 20 different demographic groups per ad.
- On 18. juni 2021
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