Artificial Intellegence and Robotics

ARTIFICIAL INTELLIGENCE & ROBOTICS

ARTIFICIAL INTELLIGENCE:

                                                                                                                                                

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligentmachines that work and react like humans. Some of the activities computers with artificial intelligence are designed for:

Ø  Speech recognition

Ø  Learning

Ø  Planning

Ø  Problem solving

“The science and engineering of making intelligent machines, especially intelligent computer programs”.          --------- John McCarthy                                                                                                                                                                                  

 Types of AI:

Strong AI:

            Strong AI is intelligence that matches or exceeds that of human intelligence.(Ultimate of AI research)

Weak AI:

            It is narrow focused than strong AI

Whole Brain Emulation:

            Mapping and recreating the human brain through neuro imaging.

Expert Systems:

The expert systems are the computer applications developed to solve complex problems in a particular domain, at the level of extra-ordinary human intelligence and expertise.

Characteristics of Expert Systems:

• High performance
• Understandable
• Reliable
• Highly responsive

Robot:

A ROBOT is a mechanical or virtual artificial agent, usually an electro-mechanical machine that is guided by a computer program or electronic circuitry.

Robots can be autonomous or semi-autonomous.

A Robot may convey a sense of intelligence or thought of its own.

AI with robots:

AI – Natural Language processing:

            Processing of Natural Language is required when you want an intelligent system like robot to perform as per your instructions, when you want to hear decision from a dialogue based clinical expert system, etc.

Here are two components of NLP as given −

1. Natural Language Understanding (NLU)
2. Natural Language Generation (NLG)  

Intelligent Systems:

The ability of a system to calculate, reason, perceive relationships and analogies, learn from experience, store and retrieve information from memory, solve problems, comprehend complex ideas, use natural language fluently, classify, generalize, and adapt new situations.

Machine Learning:

Machine Learning is refers to algorithms that enable software to improve its performance over time as it obtains more data, this is programming by input – output examples rather than just coding.

Example:

1. Programmer have no idea how to program a computer to recognize a dog.
2. But, He can create a program with a form of intelligence that can learn  to do so
3. Now, he gives the enough image data in the form of dogs and let it process and learn
4. When you give the program of an image of a new dog that it’s never seen before,
5. It would be able to tell that “it’s a dog” with relative ease.

How does Robot work

Overview:

Great to Know (Achievements):

Deep Blue: Deep Blue was a chess-playing computer developed by IBM. It is known for being the first computer chess-playing system to win both a chess game and a chess match against a reigning world champion under regular time controls

ü  In 1996 Greatest Chess Player of All Time Garry Kasparov from Russia face IBM’s Deep Blue and Garry managed to beat Deep Blue but Next year Deep Blue Won.

Deep Fritz (Super Comp): Fritz is a German chess program developed by Vasik Rajlich (engine) and Chess Base (user interface).

In 2002 Worlds Highest Ranker Player Vladimir Kramnic faced off Deep Fritz (Super comp.) Vladimir managed to draw the match.

Drawbacks:

       Limited Ability

       Difficult code

       Slow Real Time Response

    Can’t Handle Emergency Situation

Above all High in Cost

“The development of complete Artificial Intelligence could spell THE END OF HUMAN RACE”

                                    -Stephen Hawking

Robot in the Family

SIEM (Security Information and Event Management)

What is SIEM?

Security information and event management (SIEM) is an approach to security management that combines SIM (security information management) and SEM (security event management) functions into one security management system.

§  A SIEM system collects logs and other security-related documentation for analysis. Most SIEM systems work by deploying multiple collection agents in a hierarchical manner to gather security-related events from end-user devices, servers, network equipment -- and even specialized security equipment like firewalls, antivirus or intrusion prevention systems.

Why is SIEM Necessary?

Ø  Rise in data breaches due to internal and external threats.

Ø  Attackers are smart and traditional security tools just are not enough.

Ø  Mitigate sophisticated cyber-attacks.

Ø  Manages large volumes of logs from multiple sources.

Ø  Meets exact compliance requirements

Why do organizations use it?

Threat Management:

The ability to detect risky scenarios and common attacks, as well as attack paths defined by the organization itself.

Compliancy:

                Joining the logs and reports of multiple systems within the organization, enabling an easy access and analysis by a built-in framework in each system.

Forensic Support:

Ø  The information available within SIEM is very valuable from a forensic perspective and can greatly aid a forensic analyst in his or her investigation.

Ø  SIEM allows forensic analysts to search within logs of many systems in a centralized way, without the need of re-collecting the log files of compromised systems.

Protection (What and How):

Implementation of SIEM:

Requirements:

  Ø  All Unix systems need a centralized logging system to be developed for the SIEM. Databases require a great many configuration steps

  Ø  Each web server needs a new process installed to monitor the web logs. Taking logs from cloud resources on AWS involves another complex series of configuration procedure.

  Ø  SIEM requires highly skilled IT personnel resources.

  Ø  Patching, hardware refreshes and overall change management have to be coordinated with SIEM

Use Cases:

SIEM Tools:

•       Hewlett Packard Enterprise (HPE) ArcSight

•       Splunk Enterprise Security (ES)

•       IBM Security QRadar

•       AlienVault Unified Security Management (USM)

•       LogRhythm SIEM

•       McAfee Enterprise Security Manager (ESM)

•       Micro Focus Sentinel Enterprise

•       SolarWinds Log & Event Manager

•       Trustwave SIEM Enterprise and Log Management Enterprise

•       RSA NetWitness Suite

"It takes time and effort to get things set up, and this is going to be a manpower initiative proportional to the complexity of organization"