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  • How Artificial Intelligence Will Scrape More Business Information Online From Less Data

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    Is Google Duplex a magical device of artificial intelligence (AI)? 

     

    Indeed, it is a good representative of AI. You just allow it to take calls on your behalf. It will not let you feel that a computerized voice is on the other end talking to you. The real world tasks can now be carried out over the phone. In the nutshell, AI in association with voice recognition and machine learning is ready to serve natural conversational experience, as you normally speak to another person. It’s promising. It is carving the future.

     

    Future of AI

    Machines could never be as intelligent as they are today, if data did not come to their assistance.  However, the data scientists are harnessing data extraction services, pulling data from IoTs.  They extract valuable senses out of mountains of data. Now, human-like intelligence is not so far. The delay is occurring due to reliance on bottom-up approach. It pieces together datasets to give rise to more complex systems, creating more sub-systems of the emerging systems.

     

    Now, the whole attention is on developing general reasoning in machines. It’s a real challenge. The edge cases, wherein the data exist, develop neural networks (i.e. a computer system modeled on a human brain). 

     

    How Artificial Intelligence Will Scrape More Business Information Online From Less Data

     

    Challenges in Data-Driven Intelligence:

     

    1. Training machines on mountains of data to develop instinctive learning and building up systems: Making machines learn something is a crucial drawback. They require a pool of data to master that specific understanding. Let’s say, a computer passes through Turing Test while recognizing a CAPTCHA (which is Completely Automated Public Turing test to tell Computer and Humans Apart).  

     

    2. Defining and training AI on anomalies, like how to identify a man in a robot costume in the mega events: The AI of android camera mostly fails to recognise exact age and gender of the person. Likewise, the facial recognition system embedded in iPhone X doesn’t recognise morning faces, as it looks slightly drowsy and haggard with puffy eyes.

     

    3. Business Limitations: As developers discover algorithms driven from data sources, the AI will soon be integrated to develop instinctive learning. But, the data warehouses are limited. Neither can every company afford data from multiple resources. If a market research company, let’s say, wants to derive a unique marketing approach, it requires oodles of data. That data will have countless prospects to develop machine learning. But, the data-hungry systems are prone to breaching because they consume huge amount of consumer data. Consequently, the bad actors, like Cambridge Analytica, emerges into malicious attempts of manipulating data subjects’ (whom the data belong to) personal details. This is why the GDPR (General Data Protection Regulation) had to be executed from 25 May, 2018 to rescue.    

     

    Futuristic Neural Networks: The neural network represents a replica of a human brain and nervous system. The machines are made to adapt to this network. That network basically conceives general intelligence or instinctive learning. The human being has the power of cognition by birth. Now, this cognitive power is integrated with machines. 

     

    Human-Like Intelligence is on-the-way: As aforementioned, the work is on to develop conceptual understanding in machines. A human mind is capable of instinctive learning. But, it’s not a walkover to develop a similar understanding in a computer or an iPhone.

     

    Let’s reconsider the example of a CAPTCHA. A person takes a wink to understand and type a string of letters. But, that kind of neural network remains absent in machines. They are fed on data to build up understanding through rigorous training. The Vicarious model emerges as a breakthrough in this direction. It requires just 300-fold data than those of 50,000-fold data models for training those devices.

     

    Instinctive Learning:  The cognitive learning in machines empowers them to defeat uncertainty and scarcity of data. Evoke the instinctive learning approach of an infant. Within a few days, he learns how to react if the mother cuddles him. It’s a natural phenomenon. But, the internet or computers don’t have that instinct. They need to be trained.

     

    The AR and VR technologies are emerging in a lead role, deducting usage of data to cater expert knowledge. The global localization is going to be combined with augmented reality. The ARCore is a new platform that overlays direction right on the top of the Google Maps when you turn on its navigation mode.

     

    Common sense: It’s an instinctive approach to deal with presence of mind. When Google launched Street View ten years ago, it amazed the world. Gradually, it continued to stretch to expansive coverage. Now, people don’t encounter with mishaps and hassles. They can turn on the preview of the route where they want to reach. Presently, the integration of augmented reality has made it more powerful. While using the real-time route view and traffic congestion, it assists users to transmit from the less congested lane. A human mind tends to harness this common sense. But now, the apps are bringing a turning point where artificial intelligence will walk hand and hand to human behaviour.

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