AI News, The Network Gets Smarter, Simpler and More Secure with Artificial ... artificial intelligence

The network gets smarter, simpler and more secure with Artificial Intelligence and Machine Learning: Cisco

These new capabilities will grant IT teams: 'As the pace of change and diversity of the environment continues to rapidly evolve, Cisco is committed to continually simplifying our solutions,' said Osama Al-Zoubi, Chief Technology Officer, Cisco Middle East and Africa.

'Artificial intelligence and machine learning can enable businesses to efficiently discern which issues to prioritize, becoming more nimble and proactive.

Reducing Complexity with the Multidomain Network To help customers simplify the unprecedented complexity of modern IT, Cisco is building an architecture that spans every domain of the intent-based network -- campus, branch, WAN, IoT, data center and cloud.

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Artificial intelligence (AI) is a set of hardware and software systems capable of providing computing units with capabilities that, to a human observer, seem to imitate humans’ cognitive abilities. It

uses an assembly of nature-inspired computational methods to approximate complex real-world problems where mathematical or traditional modeling have proven ineffective or inaccurate.

Artificial Intelligence uses an approximation of the way the human brain reasons, using inexact and incomplete knowledge to produce actions in an adaptive way,with experience built up over time.

A widespread model assumes the raw data from sensors are sent to a powerful central remote intelligence (Cloud), thus requiring significant data bandwidth and computational capabilities.

This distributed approach significantly reduces both the required bandwidth for data transfer and the processing capabilities of cloud servers, leveraging modern computing capabilities at the edge.

Advanced sensors, such as theLSM6DSOX (IMU), contain a machine learning core, a Finite State Machine (FSM) and advanced digital functions to provide to the attached STM32 or application central system capability to transition from ultra-low power state to high performant high accuracy AI capabilities for battery operated IoT, gaming, wearable technology and consumer electronics.

Beneath the buzzwords: AI, video analytics, edge computing and more

As the dictionary definition puts it, a buzzword is: “A word or expression from a particular subject area that has become fashionable by being used a lot.” While buzzwords can get a bad press for being overused (and often misused), they usually become part of our vocabulary because they relate to new, exciting innovations with huge potential.

high-level definition of video analytics is fairly straightforward: software which automatically analyzes live and recorded video footage, providing alerts, automated responses and actionable insights.

While video analytics is not a new thing – we’ve been talking about it since the early 2000s and even before that – like many buzzwords its value is only being truly realized well after its creation as a result of other technologies catching up with the original vision.

It’s also easier to start to appreciate what “knowledge and skills” will be required for AI to be applied effectively in video analytics (for instance, at a very basic level, the ability to know when an object is a human vs.

Machine learning becomes really powerful, however, when these are analyzed alongside those of millions of other people making similar choices, and adding further known demographic information about us (from social media sites, for example).

In the world of video surveillance, machine learning has enormous potential, exemplified in a tweet from leading AI expert Andrew Ng “Pretty much everything a normal human can do in <1 second, we can now automate with AI.” This includes, of course, recognizing and classifying humans (male, female, race, age) animals, vehicles and objects.

Describing the totality of the world’s connected devices – from mobile phones to fridges, from industrial sensors to lampposts, from voice assistants to network video cameras – the IoT creates huge amounts of data every second of every day.

Of course, not all the data from the device will be useful or valuable, so its transfer and storage creates significant wasted resources in bandwidth and memory, with their associated impacts in energy consumption and cost.

This allows for a level of data analytics by the device itself – in our terms a network video camera – and therefore the transfer of only meaningful, useful data, or that data which require further analysis (for example, alerting officials of exceptions at border control requiring passport verification).

We will see smarter cameras, more able to effectively analyze situations through AI, delivering the most relevant information to operators upon which to make accurate, rapid and effective decisions regarding the appropriate response.

Hackers are turning our AI security systems against us — but they can be stopped

With the use of AI growing in almost all areas of business and industry, we have a new problem to worry about – the “hijacking” of artificial intelligence.

Steps organizations can take include paying more attention to basic security, shoring up their AI-based security systems to better detect the tactics hackers use, and educating personnel on the dangers of phishing tactics and other methods used by hackers to compromise system.

Recognizing the patterns of attack, our AI systems, based on machine learning and advanced analytics, are able to alert administrators that they are being attacked, enabling them to take action to shut down the culprits before they go too far.

Machine learning – the heart of what we call artificial intelligence today – gets “smart” by observing patterns in data, and making assumptions about what it means, whether on an individual computer or a large neural network.

So, if a specific action in computer processors takes place when specific processes are running, and the action is repeated on the neural network and/or the specific computer, the system learns that the action means that a cyber-attack has occurred, and that appropriate action needs to be taken.

Instead of trying to outfox intelligent machine-learning security systems, hackers simply “make friends” with them – using their own capabilities against them, and helping themselves to whatever they want on a server.

In one famous experiment at Kyushu University in Japan, scientists were able to fool AI-based image recognition systems nearly three quarters of the time, “convincing” them that they were looking not at a cat, but a dog or even a stealth fighter.

Stricter controls on how data is evaluated – for example, examining the timestamps on log files more closely to determine if they have been tampered with – could take from hackers a weapon that they are currently successfully using.

By shoring up their defenses against basic tactics, organizations will be able to prevent attacks of any kind – including those using advanced AI – by keeping malware and exploits off their networks altogether.

Educating employees on the dangers of responding to phishing pitches – including rewarding those who avoid them and/or penalizing those who don’t – along with stronger basic defenses like sandboxes and anti-malware systems, and more intelligent AI defense systems can go a long way to protect organizations.

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