Artificial Intelligence Takes Microsoft Jobs

Microsoft  is firing 27 journalists to replace them with Artificial Intelligence (AI), fueling the debate over whether the technology is a job creator or a destroyer. The employees were told that Microsoft’s decision to end their contracts  was taken “at short notice as part of a global shift away from humans in favor of automated updates for news”.

Microsoft will use AI to select, curate and edit news articles on the MSN and Edge homepages. The company said the journalists who maintain the news homepages on Microsoft’s MSN website and the homepage on the Edge browser are no longer needed since robots can do their jobs now.

This might prove a risky move for Microsoft, particulary for news selection on their wesites as AI will have the delicate task of t checking on abusive and harmful content, along with many minor detections of irregular and offensive content.

AI And The Future Of Jobs

Sixty years ago, the fathers of the field, Minsky and McCarthy, described AI as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task. That obviously is a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not.
AI systems will typically demonstrate at least some of the following behaviours associated with human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity.

Types of AI
At a very high level artificial intelligence can be split into two broad types: narrow AI and general AI. Narrow AI is what we see all around us in computers today: intelligent systems that have been taught or learned how to carry out specific tasks without being explicitly programmed how to do so.

This type of machine intelligence is evident in the speech and language recognition of the Siri virtual assistant on the Apple iPhone, in the vision-recognition systems on self-driving cars, in the recommendation engines that suggest products you might like based on what you bought in the past. 

Unlike humans, these systems can only learn or be taught how to do specific tasks, which is why they are called narrow AI.

Artificial general intelligence is very different, and is the type of adaptable intellect found in humans, a flexible form of intelligence capable of learning how to carry out vastly different tasks, anything from haircutting to building spreadsheets, or to reason about a wide variety of topics based on its accumulated experience. This is the sort of AI more commonly seen in movies, the likes of HAL in 2001 or Skynet in The Terminator, but which doesn't exist today and AI experts are fiercely divided over how soon it will become a reality.

There is a broad body of research in AI, much of which feeds into and complements each other. Currently enjoying something of a resurgence, machine learning is where a computer system is fed large amounts of data, which it then uses to learn how to carry out a specific task, such as understanding speech or captioning a photograph.

Neural Networks
Key to the process of machine learning are neural networks. These are brain-inspired networks of inter-connected layers of algorithms, called neurons, that feed data into each other, and which can be trained to carry out specific tasks by modifying the importance attributed to input data as it passes between the layers. During training of these neural networks, the weights attached to different inputs will continue to be varied until the output from the neural network is very close to what is desired, at which point the network will have 'learned' how to carry out a particular task.

A subset of machine learning is deep learning, where neural networks are expanded into sprawling networks with a huge number of layers that are trained using massive amounts of data. It is these deep neural networks that have fuelled the current leap forward in the ability of computers to carry out task like speech recognition and computer vision.

Another area of AI research is evolutionary computation, which borrows from Darwin's theory of natural selection, and sees genetic algorithms undergo random mutations and combinations between generations in an attempt to evolve the optimal solution to a given problem. This approach has even been used to help design AI models, effectively using AI to help build AI. This use of evolutionary algorithms to optimise neural networks is called neuro-evolution, and could have an important role to play in helping design efficient AI as the use of intelligent systems becomes more prevalent, particularly as demand for data scientists often outstrips supply.

There is barely a field of human endeavour that AI doesn't have the potential to impact and while AI won't replace all jobs, it is certain is to change the nature of work, the only question being how rapidly and how profoundly automation will alter the workplace. 

Fully autonomous self-driving vehicles aren't a reality yet, but by some predictions self-driving lorries are poised to take over 1.7 million jobs in the next decade, even without considering the impact on couriers and taxi drivers. Yet some of the easiest jobs to automate won't even require robotics. At present there are millions of people working in administration, entering and copying data between systems, chasing and booking appointments for companies.  As software gets better at automatically updating systems and flagging the information that's important, so the need for administrators will fall.

There are expert systems, where computers are programmed with rules that allow them to take a series of decisions based on a large number of inputs, allowing that machine to mimic the behaviour of a human expert in a specific domain. An example of these knowledge-based systems might be, for example, an autopilot system flying a plane and now taking journalist jobs at Microsoft.

AI is not the same as factory automation or robots, so AI is not taking away many jobs. Like the Internet, AI is an enabling technology, which is creating whole new industries. 

The first jobs created are for computer developers, AI experts and researchers, along with sales and marketing people in new AI companies. Other new jobs include educators, lawyers, and regulators to help society adjust to the changing technologies. For technologies enabled by AI such as self-driving vehicles or smart buildings, we will need construction workers, engineers, and architects to build new infrastructure. 

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