Live In AI

Artificial Neural Network Layers Artificial Neural network is typically organized in layers. Layers are being made up of many interconnected ‘nodes’ which contain an  ‘activation function’.  A neural network may contain the following 3 layers: a. Input layer The purpose of the input layer is to receive as input the values of the explanatory attributes for each observation. Usually, the number of input nodes in an input layer is equal to the number of explanatory variables. ‘input layer’ presents the patterns to the network, which communicates to one or more ‘hidden layers’. The nodes of the input layer are passive, meaning they do not change the data. They receive a single value on their input and duplicate the value to their many outputs. From the input layer, it duplicates each value and sent to all the hidden nodes. b. Hidden layer The  Hidden layers  apply given transformations to the input values inside the network. In this, incoming arcs that go...

A self-driving car (sometimes called an  autonomous car  or  driverless car ) is a vehicle that uses a combination of sensors, cameras, radar and artificial intelligence ( AI ) to travel between destinations without a human operator. To qualify as fully autonomous, a vehicle must be able to navigate without human intervention to a predetermined destination over roads that have not been adapted for its use. Companies developing and/or testing autonomous cars include Audi, BMW, Ford, Google, General Motors, Tesla, Volkswagen and Volvo. Google's test involved a fleet of self-driving cars -- including Toyota Prii and an Audi TT -- navigating over 140,000 miles of California streets and highways. AI technologies power self-driving car systems. Developers of self-driving cars use vast amounts of data from  image recognition  systems, along with  machine learning   and  neural networks , to build systems that can drive autonomously. ...

THE FUTURE OF ARTIFICIAL INTELLIGENCE Artificial intelligence is impacting the future of virtually every industry and every human being. Artificial intelligence has acted as the main driver of emerging technologies like big data, robotics and IoT, and it will continue to act as a technological innovator for the foreseeable future. THE EVOLUTION OF AI IFM is just one of countless AI innovators in a field that’s hotter than ever and getting more so all the time. Here’s a good indicator: Of the 9,100 patents received by IBM inventors in 2018, 1,600 (or nearly 18 percent) were AI-related. Here’s another: Tesla founder and tech titan Elon Musk recently donated $10 million to fund ongoing research at the non-profit research company  OpenAI  — a mere drop in the proverbial bucket if his  $1 billion co-pledge  in 2015 is any indication. And in 2017, Russian president Vladimir Putin told school children that “Whoever becomes the leader in this sphere [AI] will ...

Artificial Intelligence (AI) is everywhere. Possibility is that you are using it in one way or the other and you don’t even know about it. One of the popular applications of AI is Machine Learning (ML), in which computers, software, and devices perform via cognition (very similar to human brain). Herein, we share few examples of machine learning that we use everyday and perhaps have no idea that they are driven by ML. 1. Virtual Personal Assistants Siri, Alexa, Google Now are some of the popular examples of virtual personal assistants. As the name suggests, they assist in finding information, when asked over voice. All you need to do is activate them and ask “What is my schedule for today?”, “What are the flights from Germany to London”, or similar questions. For answering, your personal assistant looks out for the information, recalls your related queries, or send a command to other resources (like phone apps) to collect info. You can even instruct assistants for certain tasks l...

CAN AI BE DANGEROUS? Most researchers agree that a superintelligent AI is unlikely to exhibit human emotions like love or hate, and that there is no reason to expect AI to become intentionally benevolent or malevolent.  Instead, when considering how AI might become a risk, experts think two scenarios most likely: The AI is programmed to do something devastating:   Autonomous weapons are artificial intelligence systems that are programmed to kill. In the hands of the wrong person, these weapons could easily cause mass casualties. Moreover, an AI arms race could inadvertently lead to an AI war that also results in mass casualties. To avoid being thwarted by the enemy, these weapons would be designed to be extremely difficult to simply “turn off,” so humans could plausibly lose control of such a situation. This risk is one that’s present even with narrow AI, but grows as levels of AI intelligence and autonomy increase. The AI is programm...

Some of the most common application areas of AI include natural language processing, speech, and computer vision. Now, let's look at each of these in turn. Humans have the most advanced method of communication which is known as natural language. While humans can use computers to send voice and text messages to each other, computers do not innately know how to process natural language. Natural language processing is a subset of artificial intelligence that enables computers to understand the meaning of human language. Natural language processing uses machine learning and deep learning algorithms to discern a word semantic meaning. It does this by deconstructing sentences grammatically, relationally, and structurally and understanding the context of use. For instance, based on the context of a conversation, NLP can determine if the word "Cloud" is a reference to cloud computing or the mass of condensed water vapor floating in the sky.  NLP systems might also be a...

An artificial neural network is a collection of smaller units called neurons, which are computing units modeled on the way the human brain processes information. Artificial neural networks borrow some ideas from the biological neural network of the brain, in order to approximate some of its processing results. These units or neurons take incoming data like the biological neural networks and learn to make decisions over time. Neural networks learn through a process called backpropagation. Backpropagation uses a set of training data that match known inputs to desired outputs. First, the inputs are plugged into the network and outputs are determined. Then, an error function determines how far the given output is from the desired output. Finally, adjustments are made in order to reduce errors. A collection of neurons is called a layer, and a layer takes in an input and provides an output. Any neural network will have one input layer and one output layer. It will also have one or more...