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IT - AI

11 Deep Learning Software In 2022
Geekflare, June 15th, 2022
Deep learning software is revolutionizing the technology space by bringing in more accuracy and speed for data processing and making predictions and classifications.

It uses the concept of AI and ML to help businesses, organizations, research facilities, and universities gain intelligence from data and use it to drive their innovations.

The reason it's evident in this modern era is that people find solutions to ease their lives and perform tasks faster. Also, automation is taking over the world.

That said, advanced products and services created using AI, Ml, and deep learning can fulfill this demand.

Deep learning is an excellent emerging technology that can transform your business by accelerating your data analysis and predictive intelligence.

In this article, we will explore the topic more and find the best deep learning software to include in your tool kit.


7 Ways To Bring AI To Cybersecurity
DARKReading, June 15th, 2022
Academic researchers are developing projects to apply artificial intelligence to detect and stop cyberattacks and keep critical infrastructure secure, thanks to grants from the C3.ai Digital Transformation Institute.

New ransomware variants and deceptive techniques such as living off the land and store now, decrypt later are sidestepping heuristic analysis and signature-based malware detection. Behavior-based tools can compare network activity against an established norm and flag when they detect unusual and suspicious actions and patterns. Powered by artificial intelligence (AI) and machine learning (ML), such tools represent hope in a post-Colonial Pipeline world.

The term machine learning refers to a computational system that has the ability to ingest data, analyze it and spot patterns and trends.

Generally considered a subset of artificial intelligence (AI), machine learning (ML) systems generate algorithms based on a set of sample data and then deliver predictions, without being expressly programmed to do so. Moreover, these algorithms change and adapt as new data appears or conditions change.

This autonomous learning capability is at the center of today's enterprise. It's increasingly used to make important decisions and drive automation. Although ML is closely related to statistical analysis and data mining-and there are often overlaps across these disciplines-what sets ML apart is the ability to spot patterns, trends and properties that would otherwise go unnoticed or remain out of reach. Typically, ML typically focuses on known knowledge and ways to put it to use more effectively.


Ethical AI Lapses Happen When No One Is Watching
InformationWeek, June 16th, 2022
Just because you may not see errors on the part of artificial intelligence doesn't mean that things are fine. It's up to humans to look for ethical or other issues.

Transparency often plays a key role in ethical business dilemmas -- the more information we have, the easier it is to determine what are acceptable and unacceptable outcomes. If financials are misaligned, who made an accounting error? If data is breached, who was responsible for securing it and were they acting properly?

But what happens when we look for a clear source of an error or problem and there's no human to be found? That's where artificial intelligence presents unique ethical considerations.

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