The development and background of AI is important to the argument of how it works today. Since the advent and optimism of neural nets in the 1950s, huge processing power gains have been made, and in the 2010s companies began developing individual AIs. The work of IBM and Arthur Samuel in the 1960s led to the development of early AI psychology which led to the basis of AI designing today (Cecile De Jesus, 2017).Cecile references Intel, IBM, and Nvidia timelines, and important people whom contributed to AI which lead to its development today. There is also definition to the first reference of artificial intelligence in 1956, and the coining of deep learning, how it is actually defined and differences from artificial intelligence (Cecile De Jesus, 2017). However, Cecile only invested in the historical context, to truly analyze the effects of AI the issue must be viewed from a variety of variables that define the basis of the AI: ethics, policy, economics, privacy, market disruptions, and background (Stuart Russell, Daniel Dewey, Max Tegmark,2015). When researching AI, researchers must stick to a moral frame defined by multiple people in order to limit the amount of bias. The complex algorithms that are required to make an AI that seems fluent and responds to answers in a way that makes sense, can be very difficult to code without having access to compromising user information. This requires the creator to put limits on to access, which is also difficult as permissions for certain people change. The AI’s today are mostly focused on pleasing customers, and providing different services. For example, innovations in speech recognition make new AI on the cutting edge. When Apple realized that its voice recognition was lacking, it decided to recruit Acero, an independent company dedicated to natural speech in 27 different languages (David Pierce,2017). Apple over the last few years has been working to make their AI and phones superior, motivated by the profitability and the marketability of a personal assistant that can schedule all on the phone. This competitivity lead companies like Amazon to originally invest in Ivana software to create their competitive AI Alexa in 2013(Inrid Luden). These sources however do not talk about the implication of voice recognition, or the drawbacks. The competition between these different companies raises the question of transparency of data collection, how these AI are further made and updated, as well as what constitutes an AI to be special interesting and or useful.Sociability: Creation of an AI that is socially interactive, understanding of social principles, and can understand social morals and ethical dilemmas can pose many problems for AI creators. Not only are ethics something that should be researched, precautions must be taken to ensure that there are proper defenses against probable threats. The ethical debate of AI stems from privacy, as in what type of data is being collected by the AI interacted with daily, as well as how are AI influencing collection of data in the daily lives of consumers. Max Tegmark explains how AI find their individual callings based on their creators and the reason they were created. AI in general are influenced by their creators, and if AI are influenced in a negative way, the AI will follow. The possibility of killing robots is also something that must be considered (Max Tegmark,2015). The possibility of AI being made into killing machines is a possible event, and making laws to prevent against this is a heatedly debated issue. Creating an agenda for protection against negatively influenced AI is something that both Max Tegmark, and the World Economic Forum agree on. Julia Bossman, a leader of the World Economic Forum references Google, one of the leaders when it comes to artificial intelligence and face recognition to identify people, objects and places. She mentions it can go wrong, such as when a camera missed the mark on racial sensitivity, or when a software used to predict future criminals showed bias against black people (Julia Bossman, 2017). It is incredibly important that AI remain unbiased as they influence many search engines, which in turn could be influenced to provide biased answers. The point of what happens when total automation is achieved by AI is also a point brought up. In a society where AI are dominant it is imperative that AI are programmed with human style empathy, and with ethics taken into account. Nick Bostrom and Eliezer Yudkowsky in their essay explore the difference between AI and human, through study of the ethics, and of past AI as example. The human brain is “significantly more generally applicable” then that of an AI. The human ability to take into account problems and situations and judge them in a way that takes into account ethics makes humans special. To prove the point of human specialty when It comes to decision making, The pair examine the sentience and sapience, sentience being capacity to experience or “qualia”, and or the capacity to feel pain and suffer, sapience being a set of thinking capacities that are pertinent in higher intelligence (Nick Bostrom, Eliezer Yudkowsky, ND). However, they leave out the possibility of both being combined. They don’t take into account a feeling and sentient robot/AI. This poses many questions ethically, and does chip away further on the boundary of what can be considered human. The question of AI superiority does lead to the question of development. How are the AI’s rated, and for what reasons do consumers choose the different types of AI, and what are each strong at. The opinion of the public is hugely influencing upon the way the AI’s are improved, and the individual needs of the type of consumers are taken into account. In the opinion created off of social media by Digital trends, Google Assistant and Cortana are the current best bets for people who want a single “do-it-all assistant”. Alexa, however, is more useful for smart home automation and control, while Bixby is worth keeping an eye on (Digital Trends, 2017).