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  • Gavin Stone

AI and the Evolution of Working

Updated: May 27, 2018


Key Messages


- AI is growing at a huge rate, with endless application

- We need to overcome Bias and prejudice

- We need a better NZ to cope and prepare

I recently had the opportunity to attend yet another AI event aimed at demystifying the concepts and applications. The audience was many and varied and the questions that arose from the content the speakers injected were as varied as the people. I think it’s fair to say that everyone understands that AI is growing rapidly even if they can’t see the applications today, that there are still some key polarising issues to be resolved as we move forward on this journey, and that we need to better prepare our youth and country to benefit best from the opportunities presented.

There is a wealth of information (Thanks Google) that speaks directly to the growth in AI, and when you consider that exponential growth is a factor that we can not avoid with technology, its easy to see that AI is and will continue to grow in capability and adoption.

A recent Forbes article shed some light not only on the application growth in the space, but also that of Published Papers, VC Funding, number of startups, jobs etc. so you can see that there is real momentum, it’s not just hype.

https://www.forbes.com/sites/louiscolumbus/2018/01/12/10-charts-that-will-change-your-perspective-on-artificial-intelligences-growth/#2d6916254758

Apart from the issue of humanity becoming slaves to the machine, a debate that seems to divide, there are some real issues to resolve in cognitive bias and prejudice when developing AI. Bad data has always brought about bad results, and AI is not excluded from this. In a Psychology Today article they wrote that “Human cognitive bias influences AI through data, algorithms and interaction. Machine learning, a subset of AI, is the ability for computers to learn without explicit programming. AI’s learning is shaped by data, algorithms, and experience through interactions and iterations. The size, structure, collection methodology, and sources of data impact machine learning. Machine learning is dependent on the quality of learning data sets. Just like in humans, in AI the more objective the data and the larger the data set, the less possibility of distortion” We’ve seen practical applications of AI go horribly wrong and fail when we allow bias to prevail, so how do we prevent it?

Psychologytoday article

https://www.psychologytoday.com/intl/blog/the-future-brain/201802/the-human-bias-in-the-ai-machine

In April 2018 there was a research paper released that discussed bias, and the potential ways to address with de-baising techniques. It is noted that little work has been done in this area to date and significant research is needed. If you don’t have the desire to read the entire article, skip to the conclusion on page 35.


Research paper

https://arxiv.org/pdf/1804.02969.pdf


Considering all this, how do we as a nation stack up to take advantage of the change coming? A NZ forum already exists for interested parties and stakeholders to get involved in shaping our future, however it’s clear that the issues are not getting the attention they deserve, leaving us exposed to economic and societal regression. In May the forum released some information and in it they identified “The momentum in the private sector has also exposed a disconnection between universities and businesses.” and referred to the need for a national strategy to address this, and related concerns. Much of this is captured in the report “AI Shaping A Future New Zealand”. We’re #9 in the OECD for national government readiness according to Oxford Insights which may sound good, until you consider what the OECD doesn’t.


Oxford Insights

https://www.oxfordinsights.com/government-ai-readiness-index/

National strategy

https://aiforum.org.nz/2018/05/07/new-zealand-seriously-needs-a-national-ai-strategy/

report

http://resources.aiforum.org.nz/AI+Shaping+A+Future+New+Zealand+Report+2018.pdf


One of the concerns that resonates with me is how we are helping our youth to adapt to what will be a very different future when we seem to have such a significant gap not only in our focus on STEM in education, but in the human aspects that become more important in creating a hybrid future of people and machines. Numerous articles are emerging that propose success in the future workplace will be dependent on soft skills, like collaboration, critical thinking, and empathy, but these are among the top skills found lacking in new graduates. A recent post by Work Futurist Josh Catone stated that “While the hard skills required for the STEM-centric jobs of the future are important, focusing on them exclusively at the expense of developing other skills is a dangerous path”. It’s clear that we have some way to go in developing youth to take advantage of what will be a technology driven future, it’s not just about ensuring they are technically proficient.

Better in STEM and Humanity

https://x.ai/blog/steam-education-humanity/


It’s clear that AI and technology will play an important part in the future, and that there are lots of obstacles that will be addressed and ultimately overcome as it progresses. This technology revolution can’t be stopped, so we need to be determining how do we best prepare for it, as people and as a nation.


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