Why are we dubious about deep learning?

Katja Forbes

  • Managing director of Designit, Australia and New Zealand
Katja is an Australian pioneer in the field of experience design and all its components.Back in 2014, Katja founded syfte, a specialist business in research and experience design acquired by Wipro in 2018. She was then appointed Australian MD of Designit. Katja was also a co-founding member of the Interaction Design Association Board (IxDA) in Sydney, helping build a community of over 1600 designers. Today, Katja is international director on IxDA’s global board. Katja is a sought-after speaker for organisations including Women In Design, Leaders in Heels, Women In Commerce, Code like a Girl, Telstra and Macquarie Bank. Katja was recognised as a Top 10 Australian Women Entrepreneurs 2018 by My Entrepreneur Magazine and one of the 100 Women of Influence by Westpac and the Australian Financial Review in 2016.

 
The prospect of deep learning gives those of us in the industry something to get really excited about, and something to be nervous about, at the same time. 

If deep learning is artificial intelligence (AI) in its true form, it doesn’t require the interference or assistance of a human at all. This is truly revolutionary, but it can also feel a little precarious – we just can’t feel completely comfortable with learning that can be completely unsupervised. 

So should we do away with machine learning in our business, and the application of artificial intelligence that provides systems the ability to automatically carry out tasks, in favour of deep learning?  

So what is deep learning anyway? Well, think of it in relation to machine learning, which describes the process of teaching a computer to carry out a task. For example, this could be setting up your email signature so that it appears automatically each time you create, or replying to an email. Effectively, deep learning is a sub-field of machine learning, which is learning done unsupervised by a human.  

Believe it or not, deep learning has been around for quite a while, just not in the business or domestic domain, and already has a headstart in areas such as recognising speech or detecting cancer, opening up a whole range of possibilities. This gives us the idea that the computer has the ability to solve a whole host of problems that couldn’t otherwise be touched.

It is a positive result of course, but what does it mean for humanity when computers are learning things we don’t have a hope of understanding?     

Normally with machine learning, when there is a problem, we just jump in and reconfigure. With deep learning, chances are we have no idea what the computer is doing wrong, let alone how it is done right. A few options are then left to us, one of which is to destroy the faulty machine altogether, in case it begins to cause damage to its environment.   

Deep learning has been compared to the human mind, in that it can learn at the same level and speed at which the mind does, and solve much more complex problems than machine learning could ever tackle. Once again, we are delving into the stuff of Hollywood movies – will computers identified as deep learners solve problems that will enrich our lives in one way or another, or will they threaten the very fabric of our existence? Do we want to introduce something so sensitive into our work environment, something that can produce some extraordinary stuff, but also has the potential to go very wrong and destroy much more than it was even created in the first place? How would this influence our business, our clients, our livelihood? Are we brave enough to take that chance?  

Realistically, deep learning is not quite as advanced as the human mind. For example, deep learning requires a lot (as in thousands) of pictures of ‘people’ to work out how to recognise a person. So it can be easily fooled.

Will this current ‘problem’ be fixed? Most likely. The benefit is most of the work is done inside its ‘mind’ with little to no input required from a human, which is of great benefit to any business obviously until it starts to reach results that are undesired.  

At the moment, machine learning is doing the job perfectly 80 per cent of the time. This means that while you may wish it could achieve more, such as not require human-power in its learning, and perhaps some more precise results, it is likely not keeping you awake at night, something that deep learning may just do.  

Perhaps it’s better to allow deep learning to be understood better, ironed out, before introducing its remarkable ability into your workplace.  

 

 

 

 

 

 

 

 

 

 

Tags: Emerging Technologies, customer experience management, deep learning, leadership, artificial intelligence

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