In the vast expanses of dusty red Australian outback, one of the world’s largest iron ore mines is being taken over by autonomous systems. Enormous yellow haulage trucks, 7 meters tall and with no human occupants, pick their way across the engineered landscape moving materials from drill sites to crushers and out to trains a mile long. In 2015, both the drills and the trains will also become autonomous in an effort by Rio Tinto – one of the world’s largest mining concerns – to remove human operators from dangerous conditions, increase the efficiency and scale of operations, and to enable much more rapid response to changes.
Rio Tinto’s “Mine of the Future” at Pilbara, Australia hosts over 50 autonomous trucks managed from a central command station 1500 km away in Perth. The trucks have sensors, GPS, and algorithms that map the landscape and communicate continuously with central command, responding to directives and waypoints. The Mine Automation System integrates all connected components of the operation, producing real-time models with data from equipment, geologic sensors, and control algorithms in order to adapt to the dynamic conditions of their global demand chain. In 2013, the Pilabra site generated record production levels.
The Mine of the Future was “designed to respond rapidly to change.” This is perhaps the most animating feature of how the enterprise is currently evolving – and why machine intelligence is moving into business operations. We’re now inundated with the details of change at unprecedented scales. Big Data has become huge and continuous, overwhelming the human capacity to consume and process it all. Many human systems have grown too complex to manage directly. From global mining operations and supply chains to networks and enterprise tools, algorithms are proving to be much more capable at integrating complexity, responding to change, and optimizing productivity.
With near-infinite computation and waves of Big Data, older tools like convolutional networks, back propagation, and natural language processing are now making rapid advances. Record VC funding is flowing into artificial intelligence, deep learning, and sensing, showing a 302% increase in 2014. The best minds in the field have been migrating from academia into corporate product lines. Yann LeCun joined Facebook. Andrew Ng is with Baidu. Geoffrey Hinton works with Google.
Companies are now developing cognitive systems for their operations and helping commoditize machine learning as a service. IBM, Microsoft, and Google offer large, addressable machine intelligence for third party integration. Examples like IBM’s Watson Developer Cloud suggest that the rewards of AI will accrue to central players while the features will be made available to all. A host of machine learning companies have sprouted, kicking off a wave of investment and M&A. Google bought DeepMind for $400 million; Salesforce bought RelateIQ for $390 million; IBM purchased AlchemyAPI; Facebook bought wit.ai; Twitter bought MadBits; and Yahoo bought Lookflow, just to name a few.
For the enterprise, the impacts will be far-reaching. Salesforce is integrating its RelateIQ acquisition to bring machine intelligence to sales, service, and marketing. The solution watches your inbox, contacts, calendars, and social networks to analyze, filter, and predict the needs of customers. Workday’s SYMAN Insight Applications use deep learning to read employee satisfaction surveys and identify high performers who are most likely to leave the company. At larger scales, Google has been exploring neural networks to lower energy use and increase the performance of its data centers. The Port of Rotterdam, Europe’s largest, has been automating its cranes and trucks, directed by computerized managers that integrate multiple systems and align the dynamic flow of cargo from ships and stacks to trucks and trains.
Beyond optimization, machine intelligence brings the ability to integrate many isolated-but-adjacent processes into orchestrated meta-systems. How might an organization perform when sales, market & competitive intelligence, business intelligence, ERP systems, HR, IT, devops, and product innovation groups are all networked and overseen by sophisticated algorithms able to make immediate decisions without human oversight? If computation has given organizations more brainpower, and API’s and sensors are creating a nervous system, then machine intelligence offers a new layer of reasoning.
Google’s self-driving car effort is a highly-funded real-world laboratory for integrating complex systems with machine sensing and intelligence. Not only does it show the capabilities of these toolsets but it also shows how we are bridging complex real-world conditions into structured representations that can be acted upon programmatically. The self-driving car is an effort to engineer sensing, situational awareness, and adaptation into robotic systems that interact with human systems. Perhaps more profoundly, it does so at clock cycles far beyond anything humans can approach. To a multi-core CPU running at gigahertz speeds, an impending collision at 80 miles per hour looks like turtles doing ballet.
It is in this context that businesses are evolving, straining towards the forms and functions of living systems. In times of seeming acceleration and continuous change, the enterprise is starting to behave more like biology, with senses, feedback loops, fast iteration, and flexible, adaptive structures. In doing so, they participate in a virtuous cycle of funding, R&D, commercialization, and improvement of machine intelligence. If machine intelligence spreads into the gaps between nearly-ubiquitous computation and sensing, the disruptive impacts could be tremendous and the challenges and opportunities may fundamentally re-shape our world.
It’s an exciting time, with so much talent and money generating such a rich array of products and solutions – all of which has the potential to dramatically increase productivity and even reframe how we work. Data-rich enterprises can get an early start on the next wave of digital transformation by experimenting with machine intelligence to optimize performance across their value chain.
Photo credit: image of Rio Tinto autonomous haulage via Rio Tinto (brochure); Artificial intelligence and machine learning 2014 funding chart via Financial Times; Google self-driving car via Google.