The world is improving every day, inventing and discovering something new, and without these achievements we would not have progressed so far.

If you wondering the future technologies that are going to rule in 2019 and beyond, here you will get the list of some best future technologies that will revolutionize our lives.

1. The Internet of Things

The Internet of things (IoT) is the extension of Internet connectivity into physical devices and everyday objects. Embedded with electronics, Internet connectivity, and other forms of hardware (such as sensors), these devices can communicate and interact with others over the Internet, and they can be remotely monitored and controlled.

The definition of the Internet of things has evolved due to convergence of multiple technologies, real-time analytics, machine learning, commodity sensors, and embedded systems. Traditional fields of embedded systems, wireless sensor networks, control systems, automation (including home and building automation), and others all contribute to enabling the Internet of things. In the consumer market, IoT technology is most synonymous with products pertaining to the concept of the “smart home”, covering devices and appliances (such as lighting fixtures, thermostats, home security systems and cameras, and other home appliances) that support one or more common ecosystems, and can be controlled via devices associated with that ecosystem, such as smartphones and smart speakers.

The IoT concept has faced prominent criticism, especially in regards to privacy and security concerns related to these devices and their intention of pervasive presence.

 

2. Yapay Zeka Teknolojisi 

Artificial intelligence (AI), is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Computer science defines AI research as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term “artificial intelligence” is used to describe machines that mimic “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.

As machines become increasingly capable, tasks considered to require “intelligence” are often removed from the definition of AI, a phenomenon known as the AI effect. A quip in Tesler’s Theorem says “AI is whatever hasn’t been done yet.” For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology. Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomously operating cars, and intelligent routing in content delivery networks and military simulations.

 

3. Augmented Reality

Augmented reality (AR) is an interactive experience of a real-world environment where the objects that reside in the real-world are “augmented” by computer-generated perceptual information, sometimes across multiple sensory modalities, including visual, auditory, haptic, somatosensory, and olfactory. The overlaid sensory information can be constructive (i.e. additive to the natural environment) or destructive (i.e. masking of the natural environment) and is seamlessly interwoven with the physical world such that it is perceived as an immersive aspect of the real environment. In this way, augmented reality alters one’s ongoing perception of a real-world environment, whereas virtual reality completely replaces the user’s real-world environment with a simulated one. Augmented reality is related to two largely synonymous terms: mixed reality and computer-mediated reality.

 

4. Big Data

“Big data” is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volumevariety, and velocity. Other concepts later attributed with big data are veracity (i.e., how much noise is in the data)  and value.

Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. “There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem.” Analysis of data sets can find new correlations to “spot business trends, prevent diseases, combat crime and so on.” Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology and environmental research.

5. Neuro-linguistic programming (NLP)

NLP is an approach to communication, personal development, and psychotherapy created by Richard Bandler and John Grinder in California, United States in the 1970s. NLP’s creators claim there is a connection between neurological processes (neuro-), language (linguistic) and behavioral patterns learned through experience (programming), and that these can be changed to achieve specific goals in life. Bandler and Grinder also claim that NLP methodology can “model” the skills of exceptional people, allowing anyone to acquire those skills. They claim as well that, often in a single session, NLP can treat problems such as phobias, depression, tic disorders, psychosomatic illnesses, near-sightedness, allergy, common cold, and learning disorders.

NLP is marketed by some hypnotherapists and by some companies that organize seminars and workshops on management training for businesses. There is no scientific evidence supporting the claims made by NLP advocates and it has been discredited as a pseudoscience. Scientific reviews state that NLP is based on outdated metaphors of how the brain works that are inconsistent with current neurological theory and contain numerous factual errors. Reviews also found that all of the supportive research on NLP contained significant methodological flaws and that there were three times as many studies of a much higher quality that failed to reproduce the “extraordinary claims” made by Bandler, Grinder, and other NLP practitioners. Even so, NLP has been adopted by some hypnotherapists and also by companies that run seminars marketed as leadership training to businesses and government agencies.

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