What is AI and How Will it Affect Future Jobs?

Intelligent machines that are purely reactive, meaning they do not rely on past experiences to make their decisions. Examples include Googles AlphaGo, which relies on neural networks to evaluate the development of the game. What sets Reactive machines apart from more common programs such as 'Siri', which uses speech recognition to identify an appropriate pre-made response for the identified question, is that reactive machines do not have pre-made answers. Their decisions are not predetermined; instead, they are made spontaneously to react to the situation at hand.

Limited memory Artificial Intelligence, relies on its past experiences to make an evaluation of its current situation, then produces a calculated decision derived from the recent events and its original programming. Examples include driverless cars which monitor their surroundings and add any significant or unique observations to its preprogrammed representation of things like traffic lights and curves in the road.

This is the level in which the intelligence of the AI will go beyond collecting data or reacting due to algorithms and neural networks. To be a type 3 Artificial intelligence, the AI must gain an understanding of how its actions affect the emotions and actions of those around it. This means that the AI will also have some sort of consciousness (though this does depend on what one defines as a conscious).

The final stage of AI, is when the intelligence realises that not only do others have emotions and actions that can be affected but that the same thing goes for itself as well. Self-awareness is the stage in which machine intelligence will likely be impossible to tell apart from human knowledge, if not only for its superiority in various areas.

As of now, a computer system called Machine Learning, which uses statistical techniques, and machine learning algorithms to allow computers to 'learn' from data, is being used commonly in workplaces (mainly in the field of computer science). New technology being fore-fronted by Andrew Ng a Chinese computer scientist, named 'deep learning' (a subfield of Machine learning), in which machines shift through insanely big data sets to learn new behaviours. However, Machine Learning and deep learning have so far only been utilised for specific functions, and usually remain within the field of computer science to be used for tasks such as natural language processing in which machines analyse and synthesise human speech and language.

The primary fear of the those who oppose Artificial Intelligence is that instead of the current use of AI as a tool to assist people and achieve unparalleled efficiency, AI will replace people. Some do call this sci-fi nonsense, but the fear is valid. Just recently Elon Musk's OpenAI robots beat amateur DOTA2 players at their own game. Microsoft co-founder Bill Gates congratulated this as a "huge milestone in advancing AI", and while this is true, AI has been going up against humans since 1996.

This was when the famous Deep Blue vs Garry Kasparov chess match took place. Deep Blue, an IBM supercomputer played 6 games against Garry Kasparov, and lost to the world chess champion 2-4, though did beat Kasparov the very next year 3 1/2 to 2 1/2 after undergoing many improvements.

A survey of over 350 AI experts was conducted in 2018 about what we can expect of AI's capabilities in the upcoming years. They predicted the following

In addition, Martin Ford a futurist author who focuses on AI's expected impact offers the more general advice that "the best thing you can do as a worker is to make sure you're not doing something routine and repetitive and predictable". He suggests working in areas where collaboration with people, and/or being creative is a necessity. In summary, the best way to survive the automation is to be as human as possible. Choosing jobs that machines do not have the empathy or creativity to surpass humans in, or to become apart of the AI movement itself.