Introduction
In 1956, the term “Artificial Intelligence” was launched at the Dartmouth College conference, in Hanover, New Hampshire. The sector of AI endured another big winter from 1987 to 1993, corresponding with the failure of the market for some early general-purpose computers and a decline in government funding.
But research started to pick up again after that and in 1997, IBM's Deep Blue was the first machine to beat the chess champion when it conquered Russian Grandmaster Garry Kasparov. In 2011, Watson's computer giant question-answer machine captured the "Jeopardy!" quiz by defeating reigning champions, Brad Rutter and Ken Jennings.
Different Types Of Artificial Intelligence
Artificial intelligence is manufactured so that it replicates the human brain’s thinking process. Artificial intelligence can be categorized into various types depending upon how machines are compared with humans in terms of performance and versatility.
Reactive Machines
Reactive machines are the oldest types of artificial intelligence with very limited capabilities. These machines are not intelligent enough to memorize functionalities. This means that these machines cannot use their previous experiences for their present actions.
Limited Memory
This type of AI is able to learn from old authentic data to make present decisions. All the AI systems that are using deep learning are trained by a large number of data that are stored in their memory for future problems.
Theory of Mind
Theory of mind AI is the result of the progression of Reactive Machines and Limited Memory. This type of artificial intelligence is able to create a better understanding of the operations it is interacting with by knowing their beliefs, thought processes, etc.
Self-aware
Self-awareness is the last stage of AI development which exists abstractly currently. Self-aware artificial intelligence is user-friendly. It helps in developing self-awareness because of its similarity with the human brain.
Impact of Artificial Intelligence In Our Everyday Life
Artificial intelligence, by integrating with user identity verification services and biometric authentication, is becoming the most powerful weapon of this innovative world. Artificial intelligence is not easy to use. AI is implemented in our everyday life, from online shopping to the airport’s facial recognition systems.
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Google uses AI to ensure that all the emails that are landing in the user’s inbox are not spam and are authentic.
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Artificial intelligence is improving the performance of social media platforms to communicate and locate business associates and friends such as LinkedIn, Twitter, etc.
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When you type any keyword in the search bar of Google, the results that show up on the screen is because of AI in action.
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When you do online shopping, recommended products that you see are because of Artificial intelligence to personalize user experience.
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AI is used by Ride-sharing service Uber to determine how long it will take to get from your location to your destination.
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Facial recognition systems and liveliness detection are incorporated in airports, shopping malls, etc.
How To Enable Artificial Intelligence In Python?
A large number of cells present inside our brains called neurons are connected to each other by synapses to send impulses. The input gets added to all other inputs of neurons when a signal is generated by one neuron to another. The target neuron will generate an action signal forward when a given threshold is crossed. That’s how the thinking process of human beings works.
This process is modeled in the field of computer science by creating networks using matrics. Without considering all the biological complexities taken into consideration, these networks can be considered as an abstract of neurons.
Problem
Let’s say that we have a set of inputs using which we can predict the output. The table below depicts the considered problem.
Now we want to predict the following output from the given set of inputs:
Implementation
Forward Propagation
Take inputs and then multiply that by weights as follows:
Y = WiIi = W1I1+W2I2+W3I3
To calculate output, pass the result through the sigmoid formula:
1/1 + e-y
Back Propagation
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Calculate the difference between actual output and expected output to figure out the error.
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Switch weights based on the error, multiplying the input error.
W += Error Input Output (1-Output).
Here:
Output (1-Output) is the sigmoid curve derivative.
Do the repetition of the process for a few thousand iterations.
Now let’s implement the considered problem using Python. We will be using the Numpy library to calculate matrices easily.
Command to install NumPy:
Expected Result
Value of Neural Network after 10 iterations = 0.65980921
Value of Neural Network after 100 iterations = 0.87680541
Value of Neural Network after 1000 iterations = 0.9897704
Which is pretty close to the satisfactory output which is 1.
Keep Ahead Of The Uncertain
As advancement in Artificial Intelligence is taking place in this modern era of digitization, AI-threats are also increasing. Researchers have identified only a few threats, there may be thousands of ways by which Artificial Intelligence can be used for financial crimes and cyberattacks.
As we are entering a more innovative world, we need to stay ahead of artificial intelligence’s evolutionary arms race.
If artificial intelligence is integrated with user identification services and biometric authentication, it can prevent the risk of fraud at a high scale.