Machines With Humanoid Tendencies And More --Artificial Intelligenc...

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Artificial intelligence is a specialized set of software programs built to imitate the way humans assimilate and deal with complex problems relatively. Computers with AI are automated to learn higher cognitive functions like speech recognition, planning, problem solving, perception and planning. Many times we think that AI is restricted to bots trying to live human lives or take out anomalies on earth (like Ultron in Marvels Avengers). And as often as we encounter terms like Machine Learning and Artificial Intelligence (AI) images of hardwares possessing human-like knowledge and intelligence are quick to appear in our minds.

Artificial Intelligence. Image by Gerd Leonhard via Wikicommons BY-SA 2.0</div

But then again, AI is much more than a machine or hardware bearing humanoid tendency. In fact, you'd be greatly moved by how often this technology interferes in our daily lives. Retrospect back to those times when you are scrolling through your Facebook feed and multiple pop up ads appear for job vacancy or the fancy new outfit that you were thinking of buying. This leaves one thinking privacy is null and inexistent on the Internet and that this is websites spying on them. Truthfully, privacy isn’t very strong, but at the same time, websites aren’t actually spying on every individual user via pop up ads. It would be too expensive to run and illogical to do so. The phenomenon behind this is very articulatory structured algorithms that constantly receive input data via your internet usage behavior. This harnesses information from your social media feeds, search engine pages, video-sharing platforms and other websites in consensus to your activity.
Before delving into the nerdy details about algorithms, we need to know some basics about Artificial Intelligence.

An Understanding of Artificial Intelligence

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Artificial intelligence (AI) are software programs designed to mimic the way humans carry out cognitive functions like learning and solving complex problems, speech recognition, planning, perception and planning. According to Dartmouth, the term Artificial Intelligence was first birthed in the 1956 by John McCarthy at the Darthmouth conference, a conference which featured eleven (11) mathematician and ran for roughly eight (8) weeks. This event is regarded as the birth of Artificial Intelligence. However, the drive towards understanding if machines could actually have uncontrolled thinking began a quite early. Alan Turing, in 1951, proposed a test called the “Turing Test” or “The Imitation Game”. A Turing Test is a method devised by Alan Turing for determining whether or not a computer is capable of thinking like a human being.

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As decades unravelled, the field of AI went through certain vicissitudes as some AI research problems proved more difficult than anticipated, while others proved impregnable with the technologies of the time and until the late 1990s research progress in AI began to pick up the pace, researchers focused more on applying AI to real-world problems, such as image recognition and medical diagnosis. An early landmark on the success of AI was the 1997 victory of IBM’s chess-playing computer Deep Blue over then world champion Garry Kasparov.

Following this were other groundbreaking revolutions in the field, including DARPA’s (The Defense Advanced Research Projects Agency) new technology CALO (Cognitive Agent that Learns and Organizes). The introduction of CALO eventually led to the development of Apple Inc.’s Siri (a virtual assistant part of Apple Inc.'s iOS, operating systems. The assistant uses voice queries and a natural-language user interface to answer questions, make recommendations, and perform actions by delegating requests to a set of Internet services), IBM’s question-answering computer.

What Machine Learning Entails

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Discussions about AI would be futile without talking about machine learning. It is the core component that makes artificial intelligence ‘intelligent’ in all sense of the word ‘intelligent‘. Machine learning is a rubric of artificial intelligence (AI) which facilitates software applications to become more accurate in forecasting outcomes without being specially programmed to. For instance, GoogleMap, GoogleSearch and Gmail.

A major preference of machine learning is the edge that it can even be used in cases where it is unlikely or improbable to write down explicit rules to solve a problem. For example, a firm running online service might choose to use machine learning to detect user login attempts that are shady or fraudulent. The firm might start off with a set of data containing information about past login attempts, each attempt labeled fraudulent. On the basis of this data set, the firm could use machine learning to acquire a rule to apply to future login attempts that calculate which attempts are more likely to be fraudulent and thus subjected to extra security measures. We could also say, in some way that machine learning is not an algorithm for taking care of clear cut problems, but rather a more general approach to finding answers for many different problems.

Deep Learning

An Illustration Of Deep Learning. Image From Public Domain Via Wikicommons CC0</div

Over the years, the area of deep learning, also called deep network learning, has experienced massive advancements. Deep learning uses structures modelled partial after the human brain, comprising units (which are equivalent to “neurons” in the brain). Each unit incorporates a set of input values to produce an output value, which in turn is passed on to other neurons in the deeper layer of the network. For example, an image recognition application; a first layer of units might put together raw data of the image to recognize simple patterns in the image; a second layer of units might incorporate the results of the first layer to recognize patterns-of-patterns; a third layer might incorporate the results of the second layer; and so on.

Deep learning networks typically use many layers—sometimes more than one hundred (100)— and mostly use a unit in large number at each layer, to enable the recognition of extremely difficult and accurate patterns in data

How are AI and Machine Learning integrated into our everyday lives?

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Recouping on the topic of astutely built algorithms in our social media feeds, we interact with AI more often than we know. Times when Facebook automatically bring tagging suggestions on a photo that you are about to upload, facial recognition is in use. It's done by engages a very indept machine learning technique for this purpose. Amazon advertisement of recommendations for the sport shoes you were just looking up on shopping sites are functions if the harnessing of deep learning.

Conclusion

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Over the years, speculation over Terminator-like cyborgs trying to wipe out humankind has been quite common. No doubt this could happen, the technology exists to create such a merger of human and machine. In the future, there’s the possibility of having machines with a human brain. The neurons can be reproduced in a laboratory. Many advanced cognitive thinking–computer interfaces are currently being harnessed for therapeutic purposes to overcome medical or neurological problems. For instance, the (DBS) Deep Brain Stimulation electrodes are used to alleviate symptoms of Parkinson’s disease.

The human brain connected to a computer via an implant could be advantageous to patients in the short term. However, it opens up another hole in the sand, such as the legality of its use in terms of social and technological perspectives. The tugging question would be whether a computer with cognitive abilities like the brain would be regarded as a human with similar rights!