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Artificial Intelligence ™

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Artificial Intelligence | Machine Learning | Deep Learning Division of Cyberhawk Security @cyberhawksecurity #artificialintelligence #machinelearning

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Google Duplex often calls on humans for backup when making reservations on behalf of users, and that should be welcomed.  Duplex caused a stir when it debuted at Google’s I/O developer conference last year. The AI was shown calling a hair salon to make a booking and did so complete with human-like “ums” and “ahs”. The use of such human mannerisms goes to show Google’s intention was for the human to be unaware they’re in conversation with an AI. Following some outcry, Google and other tech giants have pledged to make it clear to humans if they’re not speaking to another person.  Duplex is slowly rolling out and is available for Pixel smartphone owners in the US. Currently, it turns out Duplex bookings are often being carried out by humans in call centres.  Google confirmed to the New York Times that about 25 percent of the Assistant-based calls start with a human in a call centre, while 15 percent require human intervention. Times reporters Brian Chen and Cade Metz made four sample reservations and just one was completed start to finish by the AI.  #ai #artificialintelligence #machinelearning #ml #python #datascience #datascientist #news #tensorflow #deeplearning #robot #cyberhawkai #cyberhawksecurity #neuralnetwork #news #programming #programmer #programmers #developerlife #softwareengineer #softwaredevelopment #informationtechnology #computerscience #developers @cyberhawkai @cyberhawksecurity
Reinforcement learning is an area of Machine Learning. Reinforcement. It is about taking suitable action to maximize reward in a particular situation.  It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation.  Reinforcement learning differs from the supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of training dataset, it is bound to learn from its experience.  #ai #artificialintelligence #machinelearning #ml #python #datascience #datascientist #tensorflow #deeplearning #robot #cyberhawkai #cyberhawksecurity #neuralnetwork #news #programming #programmer #programmers #developerlife #softwareengineer #softwaredevelopment #informationtechnology #computerscience #developers @cyberhawkai @cyberhawksecurity
Unsupervised learning is the training of machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. Here the task of machine is to group unsorted information according to similarities, patterns and differences without any prior training of data.  Unlike supervised learning, no teacher is provided that means no training will be given to the machine. Therefore machine is restricted to find the hidden structure in unlabeled data by our-self. For instance, suppose it is given an image having both dogs and cats which have not seen ever.  Thus the machine has no idea about the features of dogs and cat so we can’t categorize it in dogs and cats. But it can categorize them according to their similarities, patterns, and differences i.e., we can easily categorize the above picture into two parts. First first may contain all pics having dogs in it and second part may contain all pics having cats in it. Here you didn’t learn anything before, means no training data or examples.  #ai #artificialintelligence #machinelearning #ml #python #datascience #datascientist #tensorflow #deeplearning #robot #cyberhawkai #cyberhawksecurity #neuralnetwork #news #programming #programmer #programmers #developerlife #softwareengineer #softwaredevelopment #informationtechnology #computerscience #developers @cyberhawkai @cyberhawksecurity
The Kempegowda International Airport (KIA) is in talks with various domestic airlines as it puts in place a facial recognition system that will replace boarding passes of passengers. The project, titled ‘Digi Yatra’, is to be implemented in a phased manner. “We are currently finalising the integration with various airlines’ departure control systems (DCSs) to manage the check-in process using biometric ID. The first phase will be rolled out during Q3 (third quarter) of 2019,” a BIAL spokesperson told ET. The project, officials said, will enable registration at the airport using the ‘digi yatra’ kiosk at the kerbside, terminal entry using e-gates, pre-embarkation security check (PESC) using e-gates and also boarding through e-gates.  #ai #artificialintelligence #news #machinelearning #ml #python #datascience #datascientist #tensorflow #deeplearning #robot #cyberhawkai #cyberhawksecurity #neuralnetwork #news #programming #programmer #programmers #developerlife #softwareengineer #softwaredevelopment #informationtechnology #computerscience #developers @cyberhawkai @cyberhawksecurity
Supervised learning as the name indicates the presence of a supervisor as a teacher.  Basically supervised learning is a learning in which we teach or train the machine using data which is well labeled that means some data is already tagged with the correct answer. After that, the machine is provided with a new set of examples(data) so that supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labeled data.  For instance, suppose you are given an basket filled with different kinds of fruits. Now the first step is to train the machine with all different fruits one by one like this:  If shape of object is rounded and depression at top having color Red then it will be labelled as –Apple.  If shape of object is long curving cylinder having color Green-Yellow then it will be labelled as –Banana.  Now suppose after training the data, you have given a new separate fruit say Banana from basket and asked to identify it.  Since the machine has already learned the things from previous data and this time have to use it wisely. It will first classify the fruit with its shape and color and would confirm the fruit name as BANANA and put it in Banana category. Thus the machine learns the things from training data(basket containing fruits) and then apply the knowledge to test data(new fruit). #ai #artificialintelligence #machinelearning #ml #python #datascience #datascientist #tensorflow #deeplearning #robot #cyberhawkai #cyberhawksecurity #neuralnetwork #news #programming #programmer #programmers #developerlife #softwareengineer #softwaredevelopment #informationtechnology #computerscience #developers @cyberhawkai @cyberhawksecurity
Broadly, there are 3 types of Machine Learning Algorithms.. 1. Supervised Learning How it works: This algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data. Examples of Supervised Learning: Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc.  2. Unsupervised Learning How it works: In this algorithm, we do not have any target or outcome variable to predict / estimate.  It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention. Examples of Unsupervised Learning: Apriori algorithm, K-means.  3. Reinforcement Learning: How it works:  Using this algorithm, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions. Example of Reinforcement Learning: Markov Decision Process  #ai #artificialintelligence #machinelearning #ml #python #datascience #datascientist #tensorflow #deeplearning #robot #cyberhawkai #cyberhawksecurity #neuralnetwork #news #programming #programmer #programmers #developerlife #softwareengineer #softwaredevelopment #informationtechnology #computerscience #developers @cyberhawkai @cyberhawksecurity
With aims of bringing more human-like reasoning to autonomous vehicles, MIT researchers have created a system that uses only simple maps and visual data to enable driverless cars to navigate routes in new, complex environments.  Human drivers are exceptionally good at navigating roads they haven’t driven on before, using observation and simple tools. We simply match what we see around us to what we see on our GPS devices to determine where we are and where we need to go. Driverless cars, however, struggle with this basic reasoning. In every new area, the cars must first map and analyze all the new roads, which is very time consuming. The systems also rely on complex maps — usually generated by 3-D scans — which are computationally intensive to generate and process on the fly.  In a paper being presented at this week’s International Conference on Robotics and Automation, MIT researchers describe an autonomous control system that “learns” the steering patterns of human drivers as they navigate roads in a small area, using only data from video camera feeds and a simple GPS-like map. Then, the trained system can control a driverless car along a planned route in a brand-new area, by imitating the human driver.  Similarly to human drivers, the system also detects any mismatches between its map and features of the road. This helps the system determine if its position, sensors, or mapping are incorrect, in order to correct the car’s course.  #ai #datascientist #html #webdeveloper #software #programmer #programmers #java #computerscience #python #developers #webdev #webdesign #mysql #artificialintelligence #backend #elonmusk #coder #entrepreneur #machinelearning #design #softwaredeveloper #frontend #rubyonrails #development  @cyberhawkai @cyberhawksecurity
So, we choose how to represent the target function. And finally, we choose a learning algorithm to infer the target function.  So, the learning algorithm will explore the possible function parameters so that based on the training experience it can come up with the best function given its computational limitations.  So, what is very important in the designing of a learning algorithm is how to represent the target function. Before that what is important is how to represent the training experience.  #ai #datascientist #html #webdeveloper #software #programmer #programmers #java #computerscience #python #developers #webdev #webdesign #mysql #artificialintelligence #backend #elonmusk #coder #entrepreneur #machinelearning #design #softwaredeveloper #frontend #rubyonrails #development  @cyberhawkai @cyberhawksecurity
First of all, we choose the training experience or the training data. Then, we choose the target function or how we want to represent the model.  So, this is what we want to learn, the target function that is to be learned. For example, if you are trying to write a machine learning system to play the game of checkers, the target function would be, given a board position, what move to take.  And then, we want to have the class of function that we will use, the task is given a board position what move to take and we will design the function as a function of the input and we have to decide what type of function we will use, whether we will use a linear function or some other representation.  #ai #artificialintelligence #machinelearning #ml #python #datascience  #datascientist #cyberhawkai #cyberhawksecurity #neuralnetwork #news #programming #programmer #programmers #developerlife #coding #itsaihub #coderlife #codingisfun #codinglife #programminglife #softwareengineer #softwaredevelopment #codeismylife #informationtechnology #computerscience #developers  @cyberhawkai @cyberhawksecurity
At an office in HSR Layout, a box-shaped robot, mounted with a tablet, moves along the office floor while avoiding objects. As it detects a human face, it stops to greet and introduce itself: “Greetings, I’m Spod. I’m here to help you shop.” Spod is an artificial intelligence-enabled robotic shopping assistant that visitors to supermarkets may well see in near future.  At its maker’s office, Spod, still under development, manages to detect faces, greet people, introduce itself, take inputs and navigate itself. This comes close on the heels of a humanoid being deployed as a waiter at a restaurant in Shivamogga, Karnataka.  According to its makers, Spod can typically be deployed at either inventories of large e-commerce firms or at supermarkets. When customers walk into a grocery store, the robot will scan their face via a tablet mounted on it and detect if they have visited earlier. It will detect the gender and rough age of the customer, and based on these inputs, suggest products.  #ai #artificialintelligence #machinelearning #ml #python #datascience  #datascientist #cyberhawkai #cyberhawksecurity #neuralnetwork #news #programming #programmer #programmers #developerlife #coding #itsaihub #coderlife #codingisfun #codinglife #programminglife #softwareengineer #softwaredevelopment #codeismylife #informationtechnology #computerscience #developers  @cyberhawkai @cyberhawksecurity
So, this is our learning system. It is a box to which we feed the experience or the data and there is a problem or a task, that requires solution and you can also give background knowledge, which will help the system.  And for this problem or this task the learning program comes up with an answer or a solution and its corresponding performance can be measured.  So, this is the schematic diagram of a machine learning system or a learner system. Inside there are two components, two main components, the learner L and the reasoner.  See, the learner takes the experience and from that it can also take the background knowledge and from this the learner builds models and this models can be used by the reasoner, which given a task finds the solution to the task.  So, the learner takes experience and background knowledge and learns a model and the reasoner works with the model and given a new problem or task, it can come up with the solution to the task and the performance measure corresponding to this.  #ai #artificialintelligence #machinelearning #ml #python #datascience  #datascientist #cyberhawkai #cyberhawksecurity #neuralnetwork #news #programming #programmer #programmers #developerlife #coding #itsaihub #coderlife #codingisfun #codinglife #programminglife #softwareengineer #softwaredevelopment #codeismylife #informationtechnology #computerscience #developers  @cyberhawkai @cyberhawksecurity
What if drones and self-driving cars had the tingling "spidey senses" of Spider-Man?  They might actually detect and avoid objects better, says Andres Arrieta, an assistant professor of mechanical engineering at Purdue University, because they would process sensory information faster.  Better sensing capabilities would make it possible for drones to navigate in dangerous environments and for cars to prevent accidents caused by human error. Current state-of-the-art sensor technology doesn't process data fast enough -- but nature does.  And researchers wouldn't have to create a radioactive spider to give autonomous machines superhero sensing abilities.  Instead, Purdue researchers have built sensors inspired by spiders, bats, birds and other animals, whose actual spidey senses are nerve endings linked to special neurons called mechanoreceptors.  The nerve endings -- mechanosensors -- only detect and process information essential to an animal's survival. They come in the form of hair, cilia or feathers.  #ai #artificialintelligence #machinelearning #ml #python #news #programmingmemes #cyberhawkai #cyberhawksecurity #neuralnetwork #news #programming #programmer #programmers #developerlife #coding #coderlife #codingisfun #codinglife #programminglife #softwareengineer #softwaredevelopment #codeismylife #informationtechnology #computerscience #developers
A more specific example: Automated taxi driving system  Precepts:  Video, sonar, speedometer, odometer, engine sensors,  keyboard input, microphone, GPS…etc.  Actions:  Steer, accelerate, brake, horn, speak/display…etc.  Goals:  Maintain safety, reach destination, maximize profits (fuel, tire  wear),obey laws, provide passenger comfort…. etc.  Environment:  U.S. urban streets, freeways, traffic, pedestrians,weather, customers.. etc.  #ai #artificialintelligence #machinelearning #ml #python #programmingmemes #cyberhawkai #neuralnetwork #news #programming #datascience #cyberhawksecurity #programmer #programmers #developerlife #coding  #coderlife #codingisfun #codinglife #programminglife #softwareengineer #softwaredevelopment #informationtechnology #computerscience #programmingjokes #developers
# An intelligent agent perceives its environment via sensors and acts rationally upon that environment with its effectors or Actuators  Example: Human is an agent, A robot is also an agent with cameras and motors, A thermostat detecting room temperature Percept Agent’s perceptual inputs at any given instant.  # Percept sequence Complete history of everything that the agent has ever perceived  # Increasing sophistication - four types of agent system are there: * Simple Reflex Agents *Reflex Agents with an Internal State *Goal based agents *Utility based agents  # Agents are comprised of an architecture (e.g. a computer) plus a program that runs on that architecture. In this module we are primarily interested in designing the programs  # In designing intelligent systems there are four main factors to consider: 	P Percepts – the inputs to our system 	A Actions – the outputs of our system 	G Goals – what the agent is expected to achieve 	E Environment – what the agent is interacting with  A more specific example: Automated taxi driving system  # Precepts: Video, sonar, speedometer, odometer, engine sensors,  keyboard input, microphone, GPS…etc.  # Actions: Steer, accelerate, brake, horn, speak/display…etc.  # Goals: Maintain safety, reach destination, maximize profits (fuel, tire  wear),obey laws, provide passenger comfort…. etc.  # Environment: U.S. urban streets, freeways, traffic, pedestrians, weather,  customers.. etc.  #ai #artificialintelligence #machinelearning #ml #python #programmingmemes #cyberhawkai #neuralnetwork #news #programming #programmer #programmers #developerlife #coding #itsaihub #coderlife #codingisfun #codinglife #programminglife #softwareengineer #softwaredevelopment #codeismylife #informationtechnology #computerscience #developers #cyberhawksecurity
HOW IS ARTIFICIAL INTELLIGENCE USED IN PRACTICE BY COCA-COLA?  Coca-Cola serves a large number of its drinks every day through vending machines. On newer machines, typically the customer will interact through a touch-screen display, enabling them to select the product they want and even customise it with “shots” of different flavours. The company has begun fitting these machines with AI algorithms allowing them to promote drinks and flavours that are most likely to be well received in the specific locations where they are installed.  The vending machines can even alter their “mood” depending on where they are located – with machines in a shopping mall displaying a colourful, fun persona, those in a gym more focused on achieving performance, and those in a hospital appearing more functional.  Coca-Cola also uses AI to analyse social media and understand where, when and how its customers like to consume its products, as well as which products are popular in particular localities. With over 90% of consumers making purchasing decisions based on social media content, understanding how its billions of customers are discussing and interacting with the brand on platforms like Facebook, Twitter and Instagram is essential to its marketing strategy. To do this, Coca-Cola analysed engagement with over 120,000 pieces of social content to understand the demographics and behavior of its customers and those discussing the products.  Another application of AI was in securing proof of purchase for the company’s loyalty and reward schemes. When customers were asked to manually enter 14-digit product codes printed on bottle caps into websites and apps to verify their purchases, uptake was understandably low due to the unwieldy nature of the operation.  #ai #artificialintelligence #machinelearning #ml #python #programmingmemes #cyberhawkai #neuralnetwork #news #programming #programmer #programmers #developerlife #coding #itsaihub #coderlife #codingisfun #codinglife

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