13 Sep 2018
AI: are humans obsolete?
Andrew Cornell |
AI is already shifting human life. Where are we going now? Five experts give their view.
21 Sep 2018
The jobs AI is creating
In some workplaces intelligence is in short supply.
An influx of artificial intelligence is helping address this deficiency - but don’t think robots behind the counter stealing jobs (although ANZ’s own Jamie is pretty cool, even if we do say ourselves). Think simpler – it’s likely, particularly in financial services, AI use will appear largely in the form of things like digital assistants, tools to manage personal finances and automated decisions and offers.
"Jobs will be created which do not exist today as work moves up the value chain.”
Indeed, what goes unreported is the kind of skills workplaces will need when the changes wrought by AI make their way through organisations – the kind of skills only people, with jobs, can provide.
Displaced
The phrase “fourth industrial revolution” was first used in 2016 by the World Economic Forum. It is now upon us and will have significant impacts on the employee of the (not-to-distant) future.
There will be job displacement and it would be naive to think there won’t be - mostly by removing manual tasks. HOWEVER - and that’s in caps for a reason - jobs will be created which do not exist today as work moves up the value chain.
Analyst group Gartner predicts in 2020 AI will move into the black on the employment scale, creating 2.3 million jobs while eliminating 1.8 million.
As ANZ chief economist Richard Yetsenga puts it, technology is “part of the problem but also part of the solution”.
“Everybody agrees we will see tremendous disruption in labor markets but there is still a debate about who will win,” he says.
At many organisations an inevitable result of the growth of AI will be the need to take new operational models into account. Workforces will become liquid and disaggregated, among other things.
As a financial services provider ANZ expects to see increased demand for a number of specific roles.
Seven of the jobs AI will need people to do:
A study in MIT Sloan in 2017 called them “trainers, explainers and sustainers” - the three types of people AI will create greater employment demand for.
Of all the roles to develop, these seven will be critical to the success of large organisations like ANZ. The titles may differ of course but it’s not like the IT industry ever sticks to one title for any job.
- Linguists (to train meaning/detect sarcasm)
According to Peter Meliniotis of Capgemini, investment into AI-driven experiences is being noticed by consumers and starting to be embraced – yet not purely.
He says Capgemini research shows “…58 per cent of Australian consumers prefer interactions enabled by a mix of AI and humans. They want an experience informed by human intelligence.”
- Interaction modelers (for dialogue)
Artificial intelligence will still need to sound like a human to ensure a seamless customer experience.
- Worldview trainers (to train cultural differences)
Cultural sensitives are complex. As multinational companies like ANZ increasingly use AI tools, someone will need to teach the robots some manners.
- Context designer (of smart decisions)
“[AI robots] will remain poor at abstract tasks such as complex problem solving and even worse at interactions that require empathy or common sense,” a Strategy+Business report suggests.
“Thus, managing people, solving unstructured problems, and innovating will remain almost impossible to delegate to a robot, no matter how well programmed it is.”
- Data scientists & AI coders
A PWC report states “professional, scientific and technical services [will] see a 16 per cent net increase in jobs” in the UK.
“Machine learning depends on having a data-filled, accessible history from which the computer system can make inferences,” S+B says.
- Ethicists
“There are really important questions about how we embed ethics and our understanding of people’s psychology into the way we write code,” ANU College Dean of Engineering & Computer Science Eleanor Huntington recently told bluenotes.
“I think that's going to be one of the most important and profound things humans do over the next five to 10 years.”
- Evangelists & machine relations managers
Everyone needs a representative – even our robot overlords.
As far as overlords, I do jest; there are no robot overlords on the horizon. It’s a common misconception around AI. Indeed, when we refer to it we are simply talking about using machines to do things we consider to be ‘intelligent’.
This can range from using virtual assistants to respond to questions and perform tasks, to using large amounts of data to train machines to learn via algorithms, to more complex deep learning like image recognition and autonomous driving vehicles.
Global technology companies (Google, Amazon, Facebook and Apple) are already investing around 10 per cent of their revenue into intelligent automation, according to McKinsey, achieved up to 50 per cent revenue gains in key business areas as a result.
AI is expected to permanently change the financial services industry in significant ways during the coming years as institutions seek to leverage machines to become more efficient - from the front office to the back end. Estimates for an eventual reduction in back-office staff range from 20 per cent to 30 per cent.
One form of AI being adopted in local banks appears to be Chatbots. In January 2018, Commonwealth Bank of Australia launched its in-house bot Ceba to more than a million customers, performing around 200 tasks. Similar ‘virtual assistant’ functionality has also been developed at NAB.
Fraud Detection is a growing utilisation for AI in financial services. Using machine learning, systems can detect unique activities or behaviours and flag them for security teams. This is effective because of the availability of large volumes of customer data, together with transactional data updated as transactions occur.
At the other end of the complexity scale is the usage of AI to help traders perform more competently. In 2017 UBS debuted two new AI systems on the trading floor, one of which is a system which analyses reams of market data to identify trading patterns and thus formulates new strategies for trading volatility for the bank’s clients.
Ethics
AI technologies are so complex and fast-paced ethical frameworks need to be considered to tackle what lies ahead. The stigmas around automation, AI and job security will need to be carefully addressed, particular to ensure customers and staff remain engaged and trusting.
Businesses should create room for people to assume more-complex roles, moving from the manual work which dominated the pre-industrial globe to the cognitive labour which characterises strategic and administrative work in a globalized society.
There are three key ethical issues which arise.
Three ethical issues around AI:
Data Protection
What happens when an AI system is trained on one data set and applies learnings to another? Could this infringe on customer data protection and privacy rights? Who, if anyone, should own the output of AI thought processes?
Security
The more powerful a technology becomes the more can it be used for malicious reasons. Cybersecurity will become even more important.
Bias
Machine learning systems can entrench existing bias in decision-making systems. Care must be taken to ensure AI evolves to be non-discriminatory. Steps must be made to stop systemic bias.
There is an additional significant risk regarding artificial stupidity – mistakes made by machines. In the same amount of time a human can make one big mistake smart technology can potentially make many costly decisions.
Who ‘bears’ the responsibility of these risks, mistakes and consequences?
It’s a lot for large organisations to think about; indeed, some barley considered issues are as large as the one around labour displacement.
As I’ve written before, the good news is I don’t think the role of human workers will ever be truly eliminated, but the times they sure are a changing.
Maryann Jamieson is Domain Lead, Business Automation & Integration Technology at ANZ
The views and opinions expressed in this communication are those of the author and may not necessarily state or reflect those of ANZ.
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