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New LeadershipHQ book focuses on mindset and action to ‘change your world’

LEADERSHIPHQ chief executive Sonia McDonald’s recent Diversity Award, presented by NSW Minister for Women Pru Goward, was a timely precursor to this month’s release of LeadershipHQ’s second book, Leadership Attitude: How Mindset and Action can Change Your World.

Ms McDonald, its author, said the book focused on “identifying personal strengths and acting upon them, using a mentor, and empowering others, to develop leadership qualities that motivate, influence and inspire”. 

Ms McDonald said understanding personal leadership traits and attitudes were the drivers for developing leadership abilities.

“Leadership is not a role or title; it is how you think, feel and see yourself and how you act as a leader,” she said.

“Thinking like a leader generally means you will start behaving like a leader.

“I work with leaders across a wide range of industries and occupations and there is no doubt that being confident and owning who you are leads to increased leadership abilities.

“Be authentic: the best leaders are the ones that know that self-awareness is the greatest capability.”

Last year Ms McDonald was named in digital business magazine Richtopia’s 250 Most Influential Women Leaders of the world.

She is also a regular contributor to The Australian, Business Insider and Richtopia.

Leadership Attitude: How Mindset and Action can Change Your World retails for $28 and is available via the LeadershipHQ website.

The Kindle edition retails for $9.20 and is available through Amazon.

 

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Financial advisors can use tricks to manipulate major decisions by vulnerable clients – new research

A LARGE proportion of Australians are unable to tell the difference between good and bad financial advice and are unaware of techniques used by advisors to manipulate critical financial decisions, according to a major University of Sydney study.

In the wake of the study, University of Sydney Business School professor Susan Thorp has called for a tightening of regulations to protect what she calls “vulnerable” clients. 

Working with Prof. Thorp on the research project were Prof. Julie Agnew of the College of William and Mary, Virginia, USA; University of NSW’s (UNSW’s) Prof. Hazel Bateman; Dr Christine Eckert of the University of Technology Sydney (UTS) Business School; the Australian National University’s (ANU’s) Dr Fedor Iskhakov and Prof. Jordan Louviere of the University of South Australia.

Prof. Thorp said almost half of all Australians suffer from poor levels of financially literacy and many turn to financial advisors for help with decisions on such things as superannuation investments.

“Even before our research began, we were aware that many people who attend financial advisors view the advice that they are given as very good even when an objective evaluation of that advice found it not to be so,” Prof. Thorp said.

“Puzzled by this,” she said, the research team set out to “unpack the process by which this trust relationship between the advisor and the client was formed”.

The researchers produced videos of a number of advisors, some providing good and others providing bad financial advice. The videos were then shown to groups of people who were asked to identify which of the advisors they would trust.

“We found that people, on the whole, were able to tell the difference between good and bad advice on the topics that were relatively straightforward such as paying off credit card debts,” Prof. Thorp said. “But when it came to more complicated decisions, like superannuation investments, far fewer people were able to tell the difference between good and bad advice.”

TRUST MANIPULATED

The research found that trust in the advisors was easily manipulated.

“We were able to show that if an advisor gave good advice on an easy topic, that formed a good impression in the mind of the client, and they continued to trust that advisor, even when they gave them bad advice down the track,” Prof. Thorp said.

“It seems that this strategy is probably quite widely used and would be influencing people’s decision making.”

The research also measured the impact of showing clients an advisor’s qualifications.

“One of the things we were able to do in this experimental context was measure the impact of a certification and we found that displaying a qualification made people more willing to follow advice than they otherwise would be,” Prof. Thorp said.

She said clients were often unable to tell the difference between genuine and fake qualifications.

Prof. Thorp believes her research indicated a need for higher qualifications and standards for financial advisors. She has also called on the advisory industry and regulators such as ASIC to more rigorously enforce laws protecting consumers.

“A lot of people are aware of being modestly manipulated by an advisor,” Prof. Thorp concluded.

“What’s important here is that the skill gap between the client and the advisor can be large. The potential for misunderstanding or manipulation is quite high in this situation. In other words, clients are vulnerable so they need to be properly protected.”

www.sydney.edu.au

 

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Griffith Uni leads whistleblower research ‘vital for Australian business’

LEADERS of the world’s largest current research project into whistleblowing – conducted through three of Australia’s leading universities and based at Griffith University – have called for comprehensive, evidence-based law reform to maximise the benefits of whistleblowing for corporate governance and public integrity.

Yet researchers agree that the biggest challenge is not the framing of the legislation itself but the vital and difficult change of mindset required by some policymakers, organisations and the media. 

The Australian Research Council project Whistling While They Work 2: Improving managerial responses to whistleblowing in public and private sector organisations, is facing up to a wall of resistance and apathy from business.

Project leader Professor A. J.Brown said there was broad consensus that new laws and standards were needed to support whistleblowing, but as yet little guidance on what form they should take.

“As a result, given the negativity that dominates much current debate over how to respond to problems of corporate culture, regulatory capacity and whistleblower mistreatment, we risk missing some of the greatest opportunities for solving these issues,” Prof. Brown said.

“Perhaps the single greatest opportunity is the high proportion of Australian companies who already know their own people can be the best and fastest way to find out about significant problems of wrongdoing or culture – but who, like all organisations worldwide, lack clear guidance on the tools and systems needed to properly encourage and protect whistleblowing in practice.

“The same is true of regulators – it is too easy to criticise corporate leaders, attack regulators and paint a picture of whistleblowers as overwhelmingly ignored and mistreated, when we know that in both government and business, there are positive efforts and lessons, not just negative ones.

“Given the extent of consensus on the need for new legal and governance standards, it’s time to turn our attention to what those standards need to contain, to best support internal, regulatory and public whistleblowing – rather than defeat ourselves by assuming that organisations and regulators can never get it right, or that all whistleblowers are destined to suffer, no matter what.”

The Whistling While They Work 2research project is focused on identifying current and potential best practice in organisational management of whistleblowing, based on comprehensive evidence drawn from the widest possible spectrum of Australian and New Zealand organisations.

The Australian-led project stands is the largest in the world to date, and is the first to attempt systematic comparison of organisational experience in maximising whistleblowing, in a consistent way across the public and private sectors, and between countries.

Led by researchers from Griffith University,Australian National University,University of Sydney and Victoria University of Wellington, the project is supported by 22 regulatory and professional organisations including the Australian Securities and Investments Commission (ASIC),
CPA Australia,Governance Institute of Australia,Australian Institute of Company Directorsand Transparency International Australia,along with the Commonwealth Ombudsman and leading public integrity agencies in all states, including all state Ombudsmen.

In April and May, everyAustralian public sector agency and all of Australia’s 31,000 public unlisted and large proprietary companies have been formally approached by these partners and encouraged to participate in the project.

An equivalently broad approach to public and private sector organisations is also underway in New Zealand, where partners include the New Zealand State Services Commission and Ombudsman.

“This is the first time in history that integrity and regulatory authorities are known to have combined to approach every organisation in one country – let alone two – to get behind improved processes for effective disclosure and action against risks of public interest organisational wrongdoing, on such a comprehensive scale,” Prof. Brown said.

There have been two phases to the research – a threshold Survey of Organisational Processes and Procedures, which takes about 30 minutes to complete and is open to all organisations, until June 30; and a more comprehensive survey of staff, managers and systems in those organisations that elect to participate in depth, called Integrity@WERQ, setbetween August and November this year.

Individual responses from organisations are confidential to the university researchers, and participant responses in Integrity@WERQ are anonymous.

“However, aggregated results at jurisdictional, sectoral and organisational levels will provide unprecedented evidence of what is currently working, and why, or why not, in the encouragement and management of whistleblowing within organisations,” Prof Brown said.

“This is the evidence that organisations need to help them get it right, and law reformers need to know what standards should be set in new or reformed legislation, or elsewhere, including clearer and better resourced roles for independent regulators.

“For example, the research team has already resolved to place the results behind a proposal to write the replacement to the Australian Standard on Whistleblower Protection Programs (AS 8004), which was published in 2003 but is currently withdrawn.”

The determination behind the initiative was borne out by the attendees at the project’s launch, which included whistleblower Brian Hood, the former company secretary of Note Printing Australia; ASIC regional commissioner and head of the Office of the Whistleblower Warren Day; acting New South Wales Ombudsman John McMillan; and Governance Institute of Australia’s Judith Fox.

www.whistlingwhiletheywork.edu.au

 

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Reckon AI is coming? So is super-intelligence.

Technology research group Nakano’s chief analyst, Andrew Sheehy, is part of a movement to increase the business community’s understanding of what artificial intelligence (AI) is and what positives can come out of utilising AI in business. In this opinion article, he takes it a step further: superintelligence. >>  

GIVEN that the field of machine intelligence, or artificial intelligence (AI) is still clouded by controversy, then it might seem a little premature to say that ‘superintelligence’ is inevitable.

After all, many people would argue that we still do not know what human intelligence or artificial intelligence really are.

The approach I’m going to use here is to bypass completely the conventional arguments about machine intelligence and try to think about the subject in an unconventional way.

The starting point is an assumption that machine intelligence exists. The strength of the machine intelligence could be extremely weak and it could also be very limited but at this stage all we need to do is to accept that advanced AI systems like IBM Watson, or AplhaGo do indeed demonstrate at least some level of genuine intelligence.

But the moment we assume that AI systems like Watson and AlphGo exhibit some level of intelligence then we should immediately ask where that intelligence comes from?

An AI system can be divided into two parts: hardware and software.

The hardware is easiest to understand. Even the most complex computer system is untimely just a collection of semiconductors, circuit boards, passive components, plastics and metal.

It would be very hard to argue that the computer hardware itself possesses any intrinsic intelligence:

We could say that the hardware is intelligent in a way because it is the result of collective human intelligence – which was needed to conceive the design and figure out how to convert the input raw materials into the finished computer. And so if we added up all of the intelligences needed to produce the finished computer then the hardware could be said to embody that intelligence.

The problem with this is that the computer hardware, by itself, does not actually do anything, and nor can it do anything. If it cannot do anything by itself then it cannot exhibit any behaviour which we might deem ‘intelligent’.

In essence, the hardware is like a person the moment after death: intelligence was present, but it no longer is.

Hence, computer hardware as we presently understand it cannot possess intelligence.

The very most we can say about the hardware is that it is the physical environment where machine intelligence can reveal its presence.

The other part of the computer is of course the software.  But first, what exactly is ‘software’?

If this seems like a silly question then consider the following definitions:

  • Software is the part of the computer that is not physical.
  • Software refers to the programs and data used by a computer.
  • Software is encoded information and computer instructions.

But while all of these seem to be reasonable definitions, none clearly define what the software actually is.

Let’s think about this a bit more carefully.

Programmers spend their time creating software which looks like:

  function __construct($settings) {

    $this->host = $settings['host'];

    $this->db   = $settings['db'];

    $this->user = $settings['user'];

    $this->pass = $settings['pass'];

    $this->charset = $settings['charset'];

    $this->connect();

  }

  function connect() {

    try {

      // connect to database

      $this->dsn = "mysql:host=$this->host;dbname=$this->db;charset=$this->charset";

      $opt = [

          PDO::ATTR_ERRMODE  => PDO::ERRMODE_EXCEPTION,

          PDO::ATTR_DEFAULT_FETCH_MODE => PDO::FETCH_ASSOC,

          PDO::ATTR_EMULATE_PREPARES   => false,

      ];

      $this->conn = new PDO($this->dsn, $this->user, $this->pass, $opt);

      $this->status = true;

    } catch (Exception $e) {

      print "Cannot connect to database, error was: ".$e->getMessage();

    }

  }

 

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But if we think about this carefully then it becomes clear that these are simply two examples of the many ways in which the software can be represented in our physical reality.

Now I know what you’re thinking, which is that the actual software resides in the memory of the computer where it ultimately exists as a series of electronic charges, comprising electrons, that sit on the gates of millions of transistors.

But this isn’t right either.

What happens when we copy the ‘software’ from one computer to another and run the software on the second computer? At first it seems we now have two copies of the software, but do we really have two copies of the software, or merely two physical representations of the same software?

After all, given that the copy of the ‘software’ exists inside the memory of the second computer (which could be in a different country) then it must be represented by a different set of electrons.

Because none of the electrons that represented the original version of the software form part of the copy (the original electrons are still on the original computer) we can be sure that whatever the software really is, it has nothing to do with electrons.

There’s more...

Even when the software is executed on the original computer the electrons that represent a given instruction in the computer’s memory are not the same ones that are used to represent the software when that instruction is executed in the microprocessor.

Again, we see that the software is different to how it is represented.

So what, exactly, is software?

The answer is that software does not exist – at all:

Software is simply a human intellectual construct, just like mathematics.

Software can only be represented in our physical reality, say by projecting something on a computer screen, printing characters on a paper page, storing a set of electronic charges in a memory chip. Or even as oranges arranged in a particular way in a park.

This rather abstract finding is important for the following reason:

If software is just a human intellectual construct then this means that machine intelligence must be a derivative of human intelligence.

Machine intelligence is where human intellect is repurposed in a way that allows it to exist within a computer, rather than a biological brain.

By necessity, machine intelligence must be very different to human intelligence:

  • we are representing it using an imperfect intellectual construct (software)
  • we are enhancing certain aspects (by taking advantage of the computer’s ability to process large volumes of data)
  • we are ignoring certain aspects completely (like emotion etc.)
  • we are then constraining it further by it by realising it in the form of multiple, wholly independent intelligence domains (different AI systems operate within their own closed domains)
  • we are further restricting it by not providing any meaningful ability to interact with the surrounding environment

There is no way that machine intelligence is remotely like human intelligence in nature – even though it is derived from it, and – in a very restricted sense– can exhibit a degree of autonomous action or ‘free will’.

But, nevertheless, the nature of the intelligence we are creating in machines – while very different to ours – is absolutely as real as ours.

What is currently happening in the field of AI is that scientists and engineers are working with experts that have deep domain knowledge to aggregate, magnify and then commoditise their collective intelligence.

Because of the intellectual complexity of this task things are proceeding slowly on a domain-by-domain basis, but the end point is clear:

AI systems will eventually have been developed for all human intellectual domains.

We can easily imagine that these systems will be connected together into a network – rather like the internet – but where questions and answers will be relayed between the nodes, instead of data packets.

The resulting level of collective machine intelligence will be greater than the collective human intelligence that was required to build the network. This will be because the intellect of each ‘AI node’ will already be supra-human while network effects will mean that the collective intelligence of the whole network will be exponentially greater than the sum of the individual intelligences.

Even if some aspects of human intelligence – such as emotion, worry and irony, or even sentience as we understand it - remain absent from the ‘AI web’ then the resulting structure will still be extremely powerful, or ‘superintelligent’.

As soon as we realise that machine intelligence is simply a derivative, superior form of human intelligence then the inevitability of superintelligence is clear, even if the timescales and implications are not.

 

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