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Artificial intelligence


Artificial intelligence its history and its application today

Will robots take over the world? Like everything else in life, the answer is a little more complicated than ‘yes’ or ‘no’. The reality is, the concept of Artificial Intelligence (AI) is and has been around since the middle of the 20th Century. In the 1950s, Alan Turing created the Turing Test which can be used to determine a machine’s human ability. The Turing Test is still the most widely used test to determine whether or not a computer is capable of thinking like a human. Turing rightly predicted that an activity such as playing chess would be the best place to start, in the hope of a machine competing with a human. Forty-seven years later in 1997, IBM’s Deep Blue Super computer defeated Garry Kasparov, the best chess player in the world at that time.

The term Artificial Intelligence was first coined in 1956. In1969, Stanford Research Institute created Shakey, “the first electronic person”. Shakey was the first robot created which was able to combine logical reasoning and physical actions. In 1973 James Lighthill submitted a report to the UK parliament to assess the progress of AI research in the UK. This report known as the Lighthill Report concluded that “nothing can be done in AI that couldn’t be done in the other sciences.” This period between 1974 and 1980 became known as the first AI winter, which impacted Artificial Intelligence research in the UK in a negative way. Elsewhere, in Concord, Massachusetts, Symbolics’s Lisp machines were first commercialised, this was then seen as the renaissance of Artificial Intelligence. However, between 1987 and 1993, the Lisp machine market collapsed and this period became known as the second AI Winter. IBM’s victory in 1997 was the most significant achievement until Google’s speech-to-search iPhone app was built in 2008.

From there, progress has moved at a significant pace. In 2011, IBM Watson defeated Ken Jennings on Jeopardy, who won the previous 74 games. In 2011/12, Google Brain developed, with 75% accuracy, a robot which accurately identified cats on YouTube. In 2012, Apple introduced Siri, the first intelligent personal assistant. By 2013, China’s Tianhe-2 doubled the world’s top supercomputing speed. In 2014, Facebook developed DeepFace, a facial recognition system with near human accuracy. In 2015 Google developed an open source TensorFlow software library which allows for research to be shared and ideas developed through an open source platform. In 2016 Google’s DeepMind’s AlphaGo defeated Lee Sedol at Go, an ancient and very complicated Chinese board game. In that same year, China’s Sunway Taihullight tripled the world’s top supercomputing speed (93 petaflopps), while Uber piloted the first self-driving car programme in Pittsburgh, Pennsylvania.


Further to last year’s huge advancements, this year we continue to see an increase in investment in Artificial Intelligence. The technique of ‘deep reinforcement learning’, like Google’s AlphaGo, requires a computer to figure out how to navigate a maze by trial and error, without instructions or specific examples. This idea has been around for decades, but combined with a machines large (deep) neural network, this provides the power needed for the machine to solve complex problems. New training techniques have made training deep networks feasible. The rise of the internet and the vast amount of documents, images and video files available online have made training more effective. This requires a lot of number crunching power. Several research groups realised that using Graphic Processing Units (GPUs), which are specialist chips used in PCs and video games, can be used to generate graphics suited to modelling neural networks. These neural networks are then used to try and replicate the workings of the human brain. With deeper networks, more training data and powerful new hardware to make it all work, deep learning systems suddenly began making rapid progress.


Automated driving and industrial robotics use this type of learning to solve problems. China’s technology industry is shifting away from copying Western companies and has identified AI and machine learning as the next big areas of innovation. Baidu is China’s leading search engine which has had an AI focused lab for some time. Baidu has had successes in better-optimised advertising, voice recognition and natural language processing. Tencent, which offers mobile messaging and networking through WeChat, opened an AI lab last year. Didi, the ride-sharing giant that bought Uber’s Chinese operations earlier this year, is also building a lab and developing driverless cars. The Chinese government is pledging to invest $15bn in AI start-ups by 2018.


Progress has snowballed since the first industrial revolution of the late 18th Century. In less than 250 years, we have moved from horse drawn carts to self-driving cars. Some are calling the current era the Fourth Revolution. In the new digital age, AI could break the Earth’s long-held human-centric status quo. Artificial Intelligence is at the centre of the fourth revolution.

The main business advantages of AI over human intelligence are its scalability which results in huge cost savings, reduced errors and continuous improvement. AI powers the traditional sources of automation and robotics and drives progress of sectors such as autonomous vehicles and drones. AI based software has created significant business opportunities such as virtual assistants and chatbots providing expert assistance, robo-advisors in the fields of finance, insurance, legal, media and journalism, as well as expert health systems that provide medical diagnosis and assistance.

The rise of the commercial drones, which were initially restricted to military use, expanded to personal use and are now taking off for commercial purposes. These are likely to benefit industries such as manufacturing, utilities, agriculture as well as e-commerce and logistics companies such as Amazon. Despite some of the advantages of drone capability, safety and regulation needs to be addressed first, with governments across the world bringing in regulations and controls related to safety and privacy. The current industrial revolution will turn today’s manufacturing into smart factories over the next decade.

From an investment point of view, companies which do not keep pace with these developments are likely to fall behind quickly. Investing in companies which are either at the forefront of development and products of the AI world, or whose businesses will become more efficient and profitable as a result, represent an investment theme we continue to favour. Where we do hold some of the technology companies themselves, this is usually captured through pooled funds, providing a diversified investment that blends a number of themed opportunities together.

Lucy Katzarova Assistant Director, Investment Management (Charities)



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