What is Social Engineering? An illustrated presentation.Pratum
Social engineering relies profoundly on human interaction and often involves the misleading of employees into violating their organization’s security procedures. Humans are naturally helpful, but when it comes to protecting an organization’s security, being helpful to an outsider can do more harm than good.
These slides discuss social engineering, the most common attack methods, and the best means for defending against a social engineering attack.
For more helpful cyber security blog articles, visit www.integritysrc.com/blog.
Every single security company is talking about how they are using machine learning—as a security company you have to claim artificial intelligence to be even part of the conversation. However, this approach can be dangerous when we blindly rely on algorithms to do the right thing. Rather than building systems with actual security knowledge, companies are using algorithms that nobody understands and, in turn, discovering wrong insights.
In this session, we will discuss:
• Limitations of machine learning and issues of explainability
• Where deep learning should never be applied
• Examples of how the blind application of algorithms can lead to wrong results
How is ai important to the future of cyber security Robert Smith
Today’s era is driven by technology in every aspect of our lives, so much that we’ve now increased our dependence on technology on a daily basis. With an increase in the dependency, we’re now very vulnerable and exposed to the intermittent threat posed as cyber-attacks. Cyber-attack threats have plagued businesses, corporates, governments, and institutions.
Artificial Intelligence And Machine Learning PowerPoint Presentation Slides C...SlideTeam
Artificial Intelligence And Machine Learning PowerPoint Presentation Slides arrange insightful data using industry-best design practices. Highlight the differences between machine intelligence, machine learning, and deep learning through our PPT format. Utilize this PowerPoint slideshow to present advantages, disadvantages, learning techniques, and types of supervised machine learning. Further, cover the merits, demerits, and types of unsupervised machine learning. Communicate important details concerning reinforcement learning. Familiarize your viewers with the expert system in artificial intelligence. Outline examples, characteristics, constituents, uses, advantages, drawbacks, and other aspects of the expert system. Compile the deep learning process, recurrent neural networks, and convolutional neural networks through this PowerPoint theme. Present an impactful introduction to artificial intelligence. Introduce kinds, algorithms, trends, and use cases of artificial intelligence. This presentation is not only easy-to-follow but also very convenient to edit, even if you have no prior design experience. Smash the download button and start instant personalization. Our Artificial Intelligence And Machine Learning PowerPoint Presentation Slides Complete Deck are explicit and effective. They combine clarity and concise expression. https://bit.ly/3hKg7PV
What is Social Engineering? An illustrated presentation.Pratum
Social engineering relies profoundly on human interaction and often involves the misleading of employees into violating their organization’s security procedures. Humans are naturally helpful, but when it comes to protecting an organization’s security, being helpful to an outsider can do more harm than good.
These slides discuss social engineering, the most common attack methods, and the best means for defending against a social engineering attack.
For more helpful cyber security blog articles, visit www.integritysrc.com/blog.
Every single security company is talking about how they are using machine learning—as a security company you have to claim artificial intelligence to be even part of the conversation. However, this approach can be dangerous when we blindly rely on algorithms to do the right thing. Rather than building systems with actual security knowledge, companies are using algorithms that nobody understands and, in turn, discovering wrong insights.
In this session, we will discuss:
• Limitations of machine learning and issues of explainability
• Where deep learning should never be applied
• Examples of how the blind application of algorithms can lead to wrong results
How is ai important to the future of cyber security Robert Smith
Today’s era is driven by technology in every aspect of our lives, so much that we’ve now increased our dependence on technology on a daily basis. With an increase in the dependency, we’re now very vulnerable and exposed to the intermittent threat posed as cyber-attacks. Cyber-attack threats have plagued businesses, corporates, governments, and institutions.
Artificial Intelligence And Machine Learning PowerPoint Presentation Slides C...SlideTeam
Artificial Intelligence And Machine Learning PowerPoint Presentation Slides arrange insightful data using industry-best design practices. Highlight the differences between machine intelligence, machine learning, and deep learning through our PPT format. Utilize this PowerPoint slideshow to present advantages, disadvantages, learning techniques, and types of supervised machine learning. Further, cover the merits, demerits, and types of unsupervised machine learning. Communicate important details concerning reinforcement learning. Familiarize your viewers with the expert system in artificial intelligence. Outline examples, characteristics, constituents, uses, advantages, drawbacks, and other aspects of the expert system. Compile the deep learning process, recurrent neural networks, and convolutional neural networks through this PowerPoint theme. Present an impactful introduction to artificial intelligence. Introduce kinds, algorithms, trends, and use cases of artificial intelligence. This presentation is not only easy-to-follow but also very convenient to edit, even if you have no prior design experience. Smash the download button and start instant personalization. Our Artificial Intelligence And Machine Learning PowerPoint Presentation Slides Complete Deck are explicit and effective. They combine clarity and concise expression. https://bit.ly/3hKg7PV
“AI is the new electricity” proclaims Andrew Ng, co-founder of Google Brain. Just as we need to know how to safely harness electricity, we also need to know how to securely employ AI to power our businesses. In some scenarios, the security of AI systems can impact human safety. On the flip side, AI can also be misused by cyber-adversaries and so we need to understand how to counter them.
This talk will provide food for thought in 3 areas:
Security of AI systems
Use of AI in cybersecurity
Malicious use of AI
This deck is from Interpol Conference 2017, these slides shows the holistic view of machine learning in cyber security for better organization readiness
A technical seminar delivered on Machine learning in cybersecurity. Machine learning is trending and desired subject this presentation demonstrates how machine learning can be used to protect IT infrastructure
A brief history of artificial intelligence for businessJack C Crawford
Since the 1960s, Artificial Intelligence has promised us benefits in business and in our personal lives. This presentation takes us from the early days up to machine learning and applications for enterprise businesses that are delivering personalized experiences to customers ... to a "segment of one."
How Machine Learning & AI Will Improve Cyber SecurityDevOps.com
Machine Learning (ML) and Artificial Intelligence (AI) have been proclaimed as perhaps the next great leap in human quality of life, as well as a potential reason for our extinction. Somewhere in between lies how ML & AI can potentially improve our Cyber Security efforts. But are ML & AI a true panacea or merely the next shiny trinket for the cyber industry to fixate on? In this webinar we will explore:
How ML & AI are currently being utilized in cyber security efforts.
What is working and what has not worked
What is on the both the short term and near-term horizon for ML &AI
Practical steps you can take now to begin leveraging these technologies to tangibly improve your cyber security posture
Join our panel of industry experts as we explore this brave new frontier in cyber security with a candid look cutting through the hype.
Artificial Intelligence - Opportunities and Challenges for Military Modeling ...Andy Fawkes
Presented at the NATO Modelling & Simulation Symposium - Lisbon, Portugal - 19/20 October 2017. A principal theme of the NATO Science and Technology Organization (STO) is "Military Decision Making using the tools of Big Data and Artificial Intelligence (AI)". Simulation could play a significant role in addressing this theme, as it can act as a testbed for developing such concepts and support the military decision makers in future operations that are enhanced by AI. Simulation is already making a significant impact in the development of AI outside of the defence sector. Companies such as DeepMind and Nvidia are using computer games and simulations to “train” AI and autonomous systems, analogous to humans training in simulations. The rate of progress is high, driven by increases in computing power, availability of data and improved algorithms, however, AI development still faces significant technological and ethical challenges and these must be monitored and addressed as necessary.
Responsible AI in Industry: Practical Challenges and Lessons LearnedKrishnaram Kenthapadi
How do we develop machine learning models and systems taking fairness, accuracy, explainability, and transparency into account? How do we protect the privacy of users when building large-scale AI based systems? Model fairness and explainability and protection of user privacy are considered prerequisites for building trust and adoption of AI systems in high stakes domains such as hiring, lending, and healthcare. We will first motivate the need for adopting a “fairness, explainability, and privacy by design” approach when developing AI/ML models and systems for different consumer and enterprise applications from the societal, regulatory, customer, end-user, and model developer perspectives. We will then focus on the application of responsible AI techniques in practice through industry case studies. We will discuss the sociotechnical dimensions and practical challenges, and conclude with the key takeaways and open challenges.
AI, Machine Learning, and Data Science ConceptsDan O'Leary
An overview of AI, Machine Learning, and Data Science concepts, contrasting popular conceptions of AI to state-of-the-art methods in Data Science. An introduction to Machine Learning will compare supervised and unsupervised methods, give high-level descriptions of key methods, and discuss current use cases and trends.
Web version of presentation given to the Data Science Society of Auburn, a mix of undergraduate and graduate students interested in Data Science.
“AI is the new electricity” proclaims Andrew Ng, co-founder of Google Brain. Just as we need to know how to safely harness electricity, we also need to know how to securely employ AI to power our businesses. In some scenarios, the security of AI systems can impact human safety. On the flip side, AI can also be misused by cyber-adversaries and so we need to understand how to counter them.
This talk will provide food for thought in 3 areas:
Security of AI systems
Use of AI in cybersecurity
Malicious use of AI
This deck is from Interpol Conference 2017, these slides shows the holistic view of machine learning in cyber security for better organization readiness
A technical seminar delivered on Machine learning in cybersecurity. Machine learning is trending and desired subject this presentation demonstrates how machine learning can be used to protect IT infrastructure
A brief history of artificial intelligence for businessJack C Crawford
Since the 1960s, Artificial Intelligence has promised us benefits in business and in our personal lives. This presentation takes us from the early days up to machine learning and applications for enterprise businesses that are delivering personalized experiences to customers ... to a "segment of one."
How Machine Learning & AI Will Improve Cyber SecurityDevOps.com
Machine Learning (ML) and Artificial Intelligence (AI) have been proclaimed as perhaps the next great leap in human quality of life, as well as a potential reason for our extinction. Somewhere in between lies how ML & AI can potentially improve our Cyber Security efforts. But are ML & AI a true panacea or merely the next shiny trinket for the cyber industry to fixate on? In this webinar we will explore:
How ML & AI are currently being utilized in cyber security efforts.
What is working and what has not worked
What is on the both the short term and near-term horizon for ML &AI
Practical steps you can take now to begin leveraging these technologies to tangibly improve your cyber security posture
Join our panel of industry experts as we explore this brave new frontier in cyber security with a candid look cutting through the hype.
Artificial Intelligence - Opportunities and Challenges for Military Modeling ...Andy Fawkes
Presented at the NATO Modelling & Simulation Symposium - Lisbon, Portugal - 19/20 October 2017. A principal theme of the NATO Science and Technology Organization (STO) is "Military Decision Making using the tools of Big Data and Artificial Intelligence (AI)". Simulation could play a significant role in addressing this theme, as it can act as a testbed for developing such concepts and support the military decision makers in future operations that are enhanced by AI. Simulation is already making a significant impact in the development of AI outside of the defence sector. Companies such as DeepMind and Nvidia are using computer games and simulations to “train” AI and autonomous systems, analogous to humans training in simulations. The rate of progress is high, driven by increases in computing power, availability of data and improved algorithms, however, AI development still faces significant technological and ethical challenges and these must be monitored and addressed as necessary.
Responsible AI in Industry: Practical Challenges and Lessons LearnedKrishnaram Kenthapadi
How do we develop machine learning models and systems taking fairness, accuracy, explainability, and transparency into account? How do we protect the privacy of users when building large-scale AI based systems? Model fairness and explainability and protection of user privacy are considered prerequisites for building trust and adoption of AI systems in high stakes domains such as hiring, lending, and healthcare. We will first motivate the need for adopting a “fairness, explainability, and privacy by design” approach when developing AI/ML models and systems for different consumer and enterprise applications from the societal, regulatory, customer, end-user, and model developer perspectives. We will then focus on the application of responsible AI techniques in practice through industry case studies. We will discuss the sociotechnical dimensions and practical challenges, and conclude with the key takeaways and open challenges.
AI, Machine Learning, and Data Science ConceptsDan O'Leary
An overview of AI, Machine Learning, and Data Science concepts, contrasting popular conceptions of AI to state-of-the-art methods in Data Science. An introduction to Machine Learning will compare supervised and unsupervised methods, give high-level descriptions of key methods, and discuss current use cases and trends.
Web version of presentation given to the Data Science Society of Auburn, a mix of undergraduate and graduate students interested in Data Science.