Tech decision makers are (and should keep) looking for ways to effectively implement artificial intelligence into their businesses and, therefore, drive value. And though all AI technologies most definitely have their own merits, not all of them are worth investing in.
If one thing and only one thing happens after you read this article, we hope it is that you are inspired to join the 62% of companies who boosted their enterprises in 2018 by adopting Artificial Intelligence into their workflow. Now we contunue the rest of last article.
10. TEXT ANALYTICS & NLP (NATURAL LANGUAGE PROCESSING)
This technology uses text analytics to understand the structure of sentences, as well as their meaning and intention, through statistical methods and ML.
Text analytics and NLP are currently being used for security systems and fraud detection.
They are also being used by a vast array of automated assistants and apps to extract unstructured data.
11. DIGITAL TWIN/AI MODELING
A digital twin is a software construct that bridges the gap between physical systems and the digital world.
Their digital twins are basically, 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.
12. CYBER DEFENSE
Cyber defense is a computer network defense mechanism that focuses on preventing, detecting and providing timely responses to attacks or threats to infrastructure and information.
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. Seventy-six percent of the records in the survey were lost accidentally, and 69% were an identity theft type of breach.
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.
13. COMPLIANCE
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.
We are now seeing the first wave of regulatory compliance solutions that use AI to deliver efficiency through automation and comprehensive risk coverage.
Some examples of AI’s use in compliance are showing up across the world. For example, NLP (Natural Language Processing) solutions can scan regulatory text and match its patterns with a cluster of keywords to identify the changes that are relevant to an organization.
Capital stress testing solutions with predictive analytics and scenario builders can help organizations stay compliant with regulatory capital requirements. 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.
14. KNOWLEDGE WORKER AID
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.
In fact, the automation of knowledge work has been listed as the #2 most disruptive emerging tech trend.
The medical and legal professions, which are heavily reliant on knowledge workers, is where workers have been increasingly adopting AI as a diagnostic tool.
15. CONTENT CREATION
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.
16. PEER-TO-PEER NETWORKS
Peer-to-peer networks, in their purest form, are created when two or more PCs connect and share resources without the data going through a server computer.
But peer-to-peer networks are also used by cryptocurrencies, and have the potential to even solve some of the world’s most challenging problems, by collecting and analyzing large amounts of data.
17. EMOTION RECOGNITION
This technology allows software to “read” the emotions on a human face using advanced image processing or audio data processing. We are now at the point where we can capture “micro-expressions,” or subtle body language cues, and vocal intonation that betrays a person’s feelings.
Law enforcers can use this technology to try to detect more information about someone during interrogation. But it also has a wide range of applications for marketers.
There are increasing numbers of startups working in this area. Beyond Verbal analyzes, audio inputs to describe a person’s character traits, including how positive, how excited, angry or moody they are.
18. IMAGE RECOGNITION
Image recognition is the process of identifying and detecting an object or feature in a digital image or video, and AI is increasingly being stacked on top of this technology to great effect.
AI can search social media platforms for photos and compare them to a wide range of data sets to decide which ones are most relevant during image searches.
Image recognition technology can also be used to detect license plates, diagnose disease, analyze clients and their opinions and verify users based on their face.
19. MARKETING AUTOMATION
Marketing divisions have benefitted so much from AI so far, and there is great faith placed in AI within this industry for good reason. Fifty-five percent of marketers are sure AI will have a greater impact in their field that social media has. What a statement.
Marketing automation allows companies to improve engagement and increase efficiency to grow revenue faster. 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.
Adapted from Adext
