Szu Hill, EO Executives – VP & C-Suite exec search, 11th August 2017
On June 20th I had the privilege of attending the CogX Artificial Intelligence (AI) event at The Brewery in London. It was attended by leading edge experts in AI and automation, highly intelligent creators of AI technology and robotics, as well as the general public who like me are fascinated by the advance in AI and what it could mean for the future of work and society as a whole.As stated in the UK IPPR (Institute for Public Policy Research) report, advances in artificial intelligence, robotics, computing power, the Internet of Things and big data are having a profound effect on our jobs market. Approximately 15 million UK jobs are at risk of being automated by 2035, if not sooner. Institute for Public Policy Research is the UK's leading think tank. Moreover, in our post-Brexit world, a continued shortage in the availability of resources to carry out low-skilled labour at often unsociable hours and at low wages may hasten the adoption of robots in both the farming and non-farming sectors.
At this CogX AI event, the highlight of my day was meeting Sophia the Robot and Bo, the cute little robot that serves as a tour guide around the place. Sophia is Hanson Robotics’ latest and most advanced robot. She has given numerous interviews to TV & media presenters; met face-to-face with key decision makers across industries including banking, insurance & auto manufacturing; appeared onstage as a panel member and presenter in high-level conferences; as well as graced the cover of a fashion magazines and will be starring in short films too - watch out Angelina Jolie!
Could the film Ex-Machina be predicting what an AI entity like Sophia could become?
Anyway, at the core of Sophia’s make-up is artificial intelligence or AI. AI means many different things to different people, but as a whole it could be defined as intelligence exhibited by machines, when a machine displays cognitive capabilities that humans possess, such as learning, problem solving, and, carrying out advanced interactive tasks using human-like capabilities such as prediction and decision-making.
Rather than follow specifically programmed instructions, some algorithms are designed to enable computers and machines to learn on their own, coining the phrase ‘Machine Learning’. It means that computers can learn without being coded to perform specific tasks, learning through analysing data from various source and forms and using feedback from past computations to produce reliable & repeatable decisions and outcomes. The key element of ‘machine learning’ is the ability to use data gathered from different situations, to develop (or create) a solution to a novel situation. This can be thought of as literally “thinking outside the box”.
Current technological advances in internet of things, computational speed and scale, data mining & analysis to data storage/retrieval, and algorithms have combined to reduce the cost of machine-learning-based predictions significantly.
Additionally, an approach called “deep learning” (part of a broader family of machine learning methods) has been particularly important to the changes of the past five years. Deep learning is modeling neural networks in the brain and mimicking that in software/machines - so that they can process activities & tasks that requires cognitive reasoning or analysis of more than two layers deep process-wise. These neural networks and belief systems have been applied to machine vision, speech & audio recognition, social network filtering and bioinformatics where Artificial Intelligence can produce results potentially superior to human experts.
Significantly, the falling cost of prediction due to advances in machine learning has meant that many tasks previously considered unique to humans can now be done by machines, including predicting and combating fraud, staffing call centers in multiple languages, being the friendly Chat Bot that answers your queries on a website, being the friendly personal shop assistant who helps you select the most appropriate purchase for your needs, diagnosing your illness accurately, driving your car, ordering your groceries and scheduling your appointments for you.
However, to date in many roles & tasks, machine learning remains less efficient at converting data into predicted actions than the human brain. A machine may need ten-fold more data than a human does to execute the same course of action. However, once a machine has perfected the execution of an activity, it can do so at a global scale and supersonic speed that humans may not be able to do.
Algorithms can make systems smarter, but without the common sense most humans innately possess, AI still lags behind a human’s ability to make better decisions. Moreover, for many roles where human judgment is required, it is not easy to transfer the complex skill of how a human expert makes a decision onto a machine or an AI-entity. In human judgment, we often make trade-offs in our decision-making – which a machine may still not be able to do in a way that is acceptable or ethical.
If, and when, human judgment can be easily codified, then AI computers and machines may potentially begin to replace humans in the workplace on a larger scale, as well as further up the value chain … However, is this a work future that we want?
Whatever our response is to this controversial question, it remains inescapable that the C-level Executives in companies across most sectors will have to start thinking about what it means to lead and manage a hybrid work environment where men and machines are co-workers, where AI is the counterpart to your traditional human resources. Managing the AI-driven or AI-enabled workforce requires a new set of talents and expertise, and managing a firm with artificial intelligence capabilities will involve managing the artificial intelligence itself.
The challenges that lie ahead (or even exists now) may include new ways companies need to operate, business/IT/systems processes to reconfigure, data compliance minefield to navigate through, new ethical responsibilities and duties to be concerned about, new risks to mitigate, new opportunities to capitalise on, KPI’s to re-think, and many more …
What then do CEO’s, CTO/CDO’s, CSO's, COO’s, CRO’s and CHRO’s have to start planning for, and build into their strategy and execution to stay ahead of the curve in this 4th Industrial Revolution driven by AI?
We will cover these topics in my next few blogs to follow.
I would love to hear your thoughts, so feel free to comment below or contact me directly at: firstname.lastname@example.org
In the meantime, why not take a look at my recent blogs?
- SaaS Development – Why Sales and Technology Need to Be Aligned
- The SaaS Go-To-Market Stage – Why It's Critical That Sales and Technology Functions Should Continue to Work Seamlessly
- The SaaS Implementation and Post-Implementation Stage