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Adoption Picking Up for AI in Cybersecurity; More Skilled Humans Needed Too

By AI Trends Staff  AI is increasingly being put to use in the technology stacks of cybersecurity companies, but not at the expense of human experts who guide the rollout and work alongside the smart tools.  Before 2019, one in five cybersecurity software and service providers were employing AI, according to a study last year by Capgemini […]

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As AI is more widely adopted in cybersecurity, IT security professionals see it as offering assistance to humans, not replacement. (Credit: Getty Images)

By AI Trends Staff 

AI is increasingly being put to use in the technology stacks of cybersecurity companies, but not at the expense of human experts who guide the rollout and work alongside the smart tools. 

Before 2019, one in five cybersecurity software and service providers were employing AI, according to a study last year by Capgemini Research Institute, in a review of recent research published in DarkReading. Adoption was found to be “poised to skyrocket” by the end of 2020, with 63% of the firms planning to deploy AI in their solutions. Planned use in IT operations and the Internet of Things are predicted to see the most uptick. 

Increased adoption of AI does not mean that security professionals on IT staffs are ready to hand off their responsibilities. A recent study conducted by White Hat Security at the RSA Conference 2020, held live at the end of February in San Francisco, found that 60% of security professionals are more confident when cyberthreat findings are verified by humans, over those generated by AI. One-third of respondents said intuition is the most important human element fueling analysis, while 21% said creativity is an advantage for humans. 

Still, despite some reservations about AI, the White Hat survey found 70% of security professionals agreed that AI makes teams more efficient by taking over maybe 50% of the mundane tasks, freeing them for other work and reducing stress. 

Some security professionals see their jobs as too complex to be taken over by machines, according to a recent Threat Intelligence report from the Ponemon Institute. Over half of the more than 1,000 IT professionals surveyed said they would not be able to train the AI to do the tasks their teams perform, and they are more qualified than AI to catch threats in real time. For protection of networks, close to half of respondents said human intervention was a necessity. 

Nevertheless, the train has left the station for AI in cybersecurity. Some three-quarters of executives responding to the Cap Gemini survey said AI in cybersecurity speeds breach response, detection and remediation. Over 60% said AI also reduces the cost of detection and response. 

Humans Said to Need the Help of AI in Cybersecurity 

Humans need the help of AI to counter cybersecurity threats, suggests a recent report from KPMG and Oracle focused on trends in India. AI working with machine learning provides a powerful filter to sift through alerts and flag the most relevant, according to an account citing the report in The Hindu BusinessLine

“Depending only on humans to counter the threat is no longer enough. It is far easier, efficient to keep track of different threat vectors and monitor an expanding threat surface with an AI-ML led approach,” stated Greg Jensen, Senior Principal Director of Security, Oracle. “Nearly all security providers now cite the use of some form of ML in their products as a means to protect against zero-day threats and malicious behaviors that evade more traditional forms of detection,” he added. 

Greg Jensen, Senior Principal Director of Security, Oracle 

The Oracle KPMG Cloud Threat Report, based on a survey of 750 cybersecurity and IT professionals, found top priorities were the security of company financials and intellectual property. The respondents are using many products to combat threats, with 78% using more than 50 discrete cybersecurity products, and 37% using more than 100 products. 

As IT organizations in India move more operations to the cloud, many are looking to define a cloud security strategy, which frequently employs a model of shared responsibility. 

A shortage of skilled cyber security staff is a challenge for AI adoption in India, as it is globally, with not enough analysts available to triage alerts. AI is seen as being able to assist existing analysts in hunting and analyzing chains of attack. 

Over 90% of the KPMG-Oracle survey respondents acknowledged the gap between the current cloud strategies and their ability to provide effective security and privacy controls. Oracle positions to help prescribe more intelligent automation of cybersecurity incorporating AI in response. 

Unsupervised Machine Learning Seen as Effective 

Machine learning models come in these different forms: Supervised, Reinforcement, Unsupervised and Semi-Supervised (also known as Active Learning). A recent account in Technative gives the nod to Unsupervised machine learning as the preference for cybersecurity. 

Supervised Learning relies on a process of labeling in order to “understand” information. The machine learns from labeling lots of data and is able to “recognize” something only after someone, most likely a security professional, has already labeled it. The model cannot do it on its own, according to the author Ana Mezic of MixMode, a company offering a predictive threat modeling security service. 

It is not usually the case in cybersecurity that you know exactly what you are looking for. If hackers use a method of attack that the security program has not seen before, the supervised machine learning system would not recognize it. 

Unsupervised Learning draws inferences from datasets, searching for patterns out of the norm that could be dangerous. The software creates a baseline for a customer network, showing what a “normal day” looks like. A file transfer that is too large or sent at an odd time would be flagged. The model is optimized for predicting behavior, good enough that the company says it can detect zero-day attacks, those exploiting an unknown vulnerability. 

Read the source articles in DarkReadingThe Hindu BusinessLine and Technative

Source: https://www.aitrends.com/security/adoption-picking-up-for-ai-in-cybersecurity-more-skilled-humans-needed-too/

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Google AI researchers want to teach robots tasks through self-supervised reverse engineering

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A preprint paper published by Stanford University and Google researchers proposes an AI technique that predicts how goals were achieved, effectively learning to reverse-engineer tasks. They say it enables autonomous agents to learn through self-supervision, which some experts believe is a critical step toward truly intelligent systems.

Learning general policies for complex tasks often requires dealing with unfamiliar objects and scenes, and many methods rely on forms of supervision like expert demonstrations. But these entail significant tuning; demonstrations, for example, must be completed by experts many times over and recorded by special infrastructure.

That’s unlike the researchers’ proposed approach — time reversal as self-supervision (TRASS) — which predicts “reversed trajectories” to create sources of supervision that lead to a goal or goals. A home robot could leverage it to learn tasks like turning on a computer, turning a knob, or opening a drawer, or chores like setting a dining table, making a bed, and cleaning a room.

“Most manipulation tasks that one would want to solve require some understanding of objects and how they interact. However, understanding object relationships in a task-specific context is non-trivial,” explain the coauthors. “Consider the task [making a bed]. Starting from a made bed, random perturbations to the bed can crumple the blanket, which when reversed provides supervision on how to flatten and spread the blanket. Similarly, randomly perturbing objects in a clean [or] organized room will distribute the objects around the room. These trajectories reversed will show objects being placed back to their correct positions, strong supervision for room cleaning.”

VB Transform 2020 Online – July 15-17. Join leading AI executives: Register for the free livestream.

Google TRASS robot

TRASS works by collecting data given a set of goal states, applying random forces to disrupt the scene, and carefully recording each of the subsequent states. A TRASS-driven agent explores outwardly using no expert knowledge, collecting a trajectory that when reversed can teach the agent to return to the goal states. In this way, TRASS essentially trains to predict the trajectories in reverse so that the trained model can take the current state as input, providing supervision toward the goal in the form of a guiding trajectory of frames (but not actions).

At test time, a TRASS-driven agent’s objective is to reach a state in a scene that satisfies certain specified goal conditions. At every step the trajectory is recomputed to produce a high-level guiding trajectory, and the guiding trajectory decouples high-level planning and low-level control so that it can be used as indirect supervision to produce a policy via model and model-free techniques.

In experiments, the researchers applied TRASS to the problem of configuring physical Tetris-like blocks. With a real-world robot — the Kuka IIWA — and a TRASS vision model trained in simulation and then transferred to the robot, they found that TRASS successfully paired blocks it had seen during training 75% of the time and blocks it hadn’t seen 50% of the time over the course of 20 trials each.

TRASS has limitations in that it can’t be applied in cases where object deformations are irreversible, for example (think cracking an egg, mixing two ingredients, or welding two parts together). But the researchers believe it can be extended by using exploration methods driven by state novelty, among other things.

“[O]ur method … is able to predict unknown goal states and the trajectory to reach them,” they write. “This method used with visual model predictive control is capable of assembling Tetris-style blocks with a physical robot using only visual inputs, while using no demonstrations or explicit supervision.”

Source: http://feedproxy.google.com/~r/venturebeat/SZYF/~3/3Rd18kkyUUc/

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Google AI researchers want to teach robots tasks through self-supervised reverse engineering

Published

on


A preprint paper published by Stanford University and Google researchers proposes an AI technique that predicts how goals were achieved, effectively learning to reverse-engineer tasks. They say it enables autonomous agents to learn through self-supervision, which some experts believe is a critical step toward truly intelligent systems.

Learning general policies for complex tasks often requires dealing with unfamiliar objects and scenes, and many methods rely on forms of supervision like expert demonstrations. But these entail significant tuning; demonstrations, for example, must be completed by experts many times over and recorded by special infrastructure.

That’s unlike the researchers’ proposed approach — time reversal as self-supervision (TRASS) — which predicts “reversed trajectories” to create sources of supervision that lead to a goal or goals. A home robot could leverage it to learn tasks like turning on a computer, turning a knob, or opening a drawer, or chores like setting a dining table, making a bed, and cleaning a room.

“Most manipulation tasks that one would want to solve require some understanding of objects and how they interact. However, understanding object relationships in a task-specific context is non-trivial,” explain the coauthors. “Consider the task [making a bed]. Starting from a made bed, random perturbations to the bed can crumple the blanket, which when reversed provides supervision on how to flatten and spread the blanket. Similarly, randomly perturbing objects in a clean [or] organized room will distribute the objects around the room. These trajectories reversed will show objects being placed back to their correct positions, strong supervision for room cleaning.”

VB Transform 2020 Online – July 15-17. Join leading AI executives: Register for the free livestream.

Google TRASS robot

TRASS works by collecting data given a set of goal states, applying random forces to disrupt the scene, and carefully recording each of the subsequent states. A TRASS-driven agent explores outwardly using no expert knowledge, collecting a trajectory that when reversed can teach the agent to return to the goal states. In this way, TRASS essentially trains to predict the trajectories in reverse so that the trained model can take the current state as input, providing supervision toward the goal in the form of a guiding trajectory of frames (but not actions).

At test time, a TRASS-driven agent’s objective is to reach a state in a scene that satisfies certain specified goal conditions. At every step the trajectory is recomputed to produce a high-level guiding trajectory, and the guiding trajectory decouples high-level planning and low-level control so that it can be used as indirect supervision to produce a policy via model and model-free techniques.

In experiments, the researchers applied TRASS to the problem of configuring physical Tetris-like blocks. With a real-world robot — the Kuka IIWA — and a TRASS vision model trained in simulation and then transferred to the robot, they found that TRASS successfully paired blocks it had seen during training 75% of the time and blocks it hadn’t seen 50% of the time over the course of 20 trials each.

TRASS has limitations in that it can’t be applied in cases where object deformations are irreversible, for example (think cracking an egg, mixing two ingredients, or welding two parts together). But the researchers believe it can be extended by using exploration methods driven by state novelty, among other things.

“[O]ur method … is able to predict unknown goal states and the trajectory to reach them,” they write. “This method used with visual model predictive control is capable of assembling Tetris-style blocks with a physical robot using only visual inputs, while using no demonstrations or explicit supervision.”

Source: http://feedproxy.google.com/~r/venturebeat/SZYF/~3/3Rd18kkyUUc/

Continue Reading

AI

Google AI researchers want to teach robots tasks through self-supervised reverse engineering

Published

on


A preprint paper published by Stanford University and Google researchers proposes an AI technique that predicts how goals were achieved, effectively learning to reverse-engineer tasks. They say it enables autonomous agents to learn through self-supervision, which some experts believe is a critical step toward truly intelligent systems.

Learning general policies for complex tasks often requires dealing with unfamiliar objects and scenes, and many methods rely on forms of supervision like expert demonstrations. But these entail significant tuning; demonstrations, for example, must be completed by experts many times over and recorded by special infrastructure.

That’s unlike the researchers’ proposed approach — time reversal as self-supervision (TRASS) — which predicts “reversed trajectories” to create sources of supervision that lead to a goal or goals. A home robot could leverage it to learn tasks like turning on a computer, turning a knob, or opening a drawer, or chores like setting a dining table, making a bed, and cleaning a room.

“Most manipulation tasks that one would want to solve require some understanding of objects and how they interact. However, understanding object relationships in a task-specific context is non-trivial,” explain the coauthors. “Consider the task [making a bed]. Starting from a made bed, random perturbations to the bed can crumple the blanket, which when reversed provides supervision on how to flatten and spread the blanket. Similarly, randomly perturbing objects in a clean [or] organized room will distribute the objects around the room. These trajectories reversed will show objects being placed back to their correct positions, strong supervision for room cleaning.”

VB Transform 2020 Online – July 15-17. Join leading AI executives: Register for the free livestream.

Google TRASS robot

TRASS works by collecting data given a set of goal states, applying random forces to disrupt the scene, and carefully recording each of the subsequent states. A TRASS-driven agent explores outwardly using no expert knowledge, collecting a trajectory that when reversed can teach the agent to return to the goal states. In this way, TRASS essentially trains to predict the trajectories in reverse so that the trained model can take the current state as input, providing supervision toward the goal in the form of a guiding trajectory of frames (but not actions).

At test time, a TRASS-driven agent’s objective is to reach a state in a scene that satisfies certain specified goal conditions. At every step the trajectory is recomputed to produce a high-level guiding trajectory, and the guiding trajectory decouples high-level planning and low-level control so that it can be used as indirect supervision to produce a policy via model and model-free techniques.

In experiments, the researchers applied TRASS to the problem of configuring physical Tetris-like blocks. With a real-world robot — the Kuka IIWA — and a TRASS vision model trained in simulation and then transferred to the robot, they found that TRASS successfully paired blocks it had seen during training 75% of the time and blocks it hadn’t seen 50% of the time over the course of 20 trials each.

TRASS has limitations in that it can’t be applied in cases where object deformations are irreversible, for example (think cracking an egg, mixing two ingredients, or welding two parts together). But the researchers believe it can be extended by using exploration methods driven by state novelty, among other things.

“[O]ur method … is able to predict unknown goal states and the trajectory to reach them,” they write. “This method used with visual model predictive control is capable of assembling Tetris-style blocks with a physical robot using only visual inputs, while using no demonstrations or explicit supervision.”

Source: http://feedproxy.google.com/~r/venturebeat/SZYF/~3/3Rd18kkyUUc/

Continue Reading

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