Thinking machines: business opportunity or threat to humanity?

If you ask anything of a digital assistant like Siri, Cortana, Alexa or Google Now, you are already talking to some form of artificial intelligence

AI is fast becoming a day to day reality as it is introduced into connected vehicles, smart city infrastructure, big data analysis, self-healing processes and pre-emptive maintenance of connected devices. Tractica forecasts that the market for AI systems for enterprise applications will grow at 50% CAGR from $202 million in 2015 to $11 billion by 2024.

These deployments mesh well with artificial intelligence research in neural networks – things like IBM Watson, Microsoft AIX or Google DeepMind - and together these technologies will have fundamental transformative implications across enterprise and wider society. You’ll already find IBM Watson in Formula One racing pits.

The future potential of artificial intelligence is likely to be profound.  Already machines can beat human champions at tough games like Chess or Go. The latter victory was highly significant, however, as it required "the development of true intuition,” according to Lucas Baker, a software engineer at DeepMind in Medium post about the event. 

When IBM’s Deep Blue beat chess grandmaster Garry Kasparov in 1997, the software was able to predict every possible position and win through this, but the infinite complexity of Go required the machine emulate human intuition. “AlphaGo learned Go much as a human would: through observation, experimentation, self-improvement, the organic development of heuristics, and, ultimately, a keen feeling for master play,” Baker said. This is the kind of intuitive AI its developers hope will help scientists find solutions to the challenges of climate change, disease and healthcare – as well as improving digital assistant technologies such as Siri or Google Now. 

Helping hand or threat to humanity?

Philosopher Nick Bostrom, author of Superintelligence: Paths, Dangers, Strategies and leader of Future of Humanity Institute at University of Oxford, sees the prospect of superintelligent machines as “the most important and most daunting challenge humanity has ever faced.” He is concerned that if this challenge is not effectively met, then malevolent or indifferent artificial intelligence (AI) could destroy us all. This opinion has been echoed by Elon Musk, Bill Gates and Stephen Hawking. Few would suggest that AI should be banned.  One approach to harnessing AI responsibly is championed by Lukas Biewald, CEO of CrowdFlower is to develop AI solutions that also depend on a human element – like humans assessing blog posts for inappropriate content or detecting relevance.

These technologies are available across the board, with cloud computing enabling even smaller enterprises to secure the massive computing power they need in order to field artificial intelligence and deep learning into their business. Somatic founder and CEO, Jason Toy, thinks the benefits of AI will eventually be available to everyone: “Imagine that if you had an idea for a deep learning model, and you could get an initial prototype up by yourself in hours instead of months,” he told Cloud Academy.

Google support

Supporting this, Google recently published information designed to assist enterprise developers in using the search giant’s machine learning and cloud platform technologies to build online recommendation engines. The company offers an example solution of an engine capable of recommending houses to users based on what they’ve looked at before. Components include a friendly front end for data capture, permanent data storage and machine learning components equipped to manage Hadoop and Spark data sets. This follows Google’s decision to make its TensorFlow machine learning tech available to open source developers. The company also uses these technologies to build and train models with which to predict financial markets.  

AI technologies are already in everyday use worldwide. In medicine, machines are being taught to diagnose diseases by mapping medical images to disease states. These are already outperforming an expert panel at a rate of three to one. 

Enterprises are also discovering the value of data analysis and automation. Deep learning systems are being applied to a diversity of tasks, including medical diagnostic systems, credit scoring (MasterCard uses Watson), program trading, fraud detection, product recommendations, image classification, speech recognition, language translation, and self-driving vehicles. The perfect storm between affordable cloud services, big data analysis, connected devices and artificial intelligence means these technologies will become available to an ever-growing congregation of enterprise owners and SME’s. This will create profound change in how we are used to working. Stephen Pratt, ex-leader of IBM’s Watson team, says AI will become the “biggest competitive differentiators for business.”

Tractica forecasts that the market for AI systems for enterprise applications will grow at 50% CAGR from $202 million in 2015 to $11 billion by 2024.



Jon Evans

Jon Evans is a highly experienced technology journalist and editor. He has been writing for a living since 1994. These days you might read his daily regular Computerworld AppleHolic and opinion columns. Jon is also technology editor for men's interest magazine, Calibre Quarterly, and news editor for MacFormat magazine, which is the biggest UK Mac title. He's really interested in the impact of technology on the creative spark at the heart of the human experience. In 2010 he won an American Society of Business Publication Editors (Azbee) Award for his work at Computerworld.