5 misconceptions of Artificial Intelligence

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Fueling the future of business, Gartner debunked  five Artificial Intelligence (AI) misconceptions while discussing trends at its Data & Analytics Summit last week.

For many in the private sector, the picture on how that integration looks for them is still a little hazy.   “It is crucial that business and IT leaders fully understand how AI can create value for their business and where its limitations lie,”  said Alexander Linden, research vice president at Gartner.   “AI technologies can only deliver value if they are part of the organisation’s strategy and used in the right way.”

Myth No.1: AI Works in the Same Way the Human Brain Does
AI is a computer engineering discipline, comprised of software tools with problem solving its main objective. pexels-photo-590701“Some forms of machine learning (ML) – a category of AI – may have been inspired by the human brain, but they are not equivalent,” Mr Linden said.“Image recognition technology, for example, is more accurate than most humans, but is of no use when it comes to solving a math problem.  The rule with AI today is that it solves one task exceedingly well, but if the conditions of the task change only a bit, it fails.”


Myth No. 2: Intelligent Machines Learn on Their Own
Human intervention is required to develop an AI-based machine or system – from human data scientists framing the problem, triaging data, and most importantly, updating the software to enable the integration of new knowledge and data into the next learning cycle.

Myth No. 3: AI Can Be Free of Bias
“Today, there is no way to completely banish bias, however, we have to try to reduce it to a minimum,” Mr. Linden said. “In addition to technological solutions, such as diverse datasets, it is also crucial to ensure diversity in the teams working with the AI, and have team members review each other’s work. This simple process can significantly reduce selection and confirmation bias.”

Myth No. 4: AI Will Only Replace Repetitive Jobs That Don’t Require Advanced Degrees
AI enables businesses to make more accurate decisions via predictions, classifications and clustering.  These abilities have allowed AI-based solutions to replace mundane tasks, but also augment remaining complex tasks.  An example is the use of imaging AI in healthcare. pexels-photo-414860A chest X-ray application based on AI can detect diseases faster than radiologists.   In the financial and insurance industry, roboadvisors are being used for wealth management or fraud detection.   Those capabilities don’t eliminate human involvement in those tasks but will rather have humans deal with unusual cases.  With the advancement of AI in the workplace, business and IT leaders should adjust job profiles and capacity planning as well as offer retraining options for existing staff.

Myth No. 5: Not Every Business Needs an AI Strategy
In many ways, avoiding AI exploitation is the same as giving up the next phase of automation, which ultimately could place organisations at a competitive disadvantage.  “Even if the current strategy is ‘no AI’, this should be a conscious decision based on research and consideration. And – as every other strategy- it should be periodically revisited and changed according to the organisation’s needs.   AI might be needed sooner than expected,” Mr. Linden concluded.


The University of Adelaide established the Australian first institute for machine learning in response to the projected $15.7 trillion this sector is expected to contribute to the economy in the next 10 years and increasing business productivity by up to 40%.  It’s an irresistible lure for business.

FutureNation likens the new AI race to a inverse industrial revolution.  Many smaller independent players are at the epicentre of creative innovations that capture data and he says its true potential will be in the way it is statistically analysed and the inferentials derived from the exabytes data mines.   Hence the power of AI lies in the algorithms.

So where does that leave us normal everyday folk?  Is it really that big of a deal?  We’ve been flying on planes for decades now with autopilots, enjoying shorter wait times with the airport e-gates, we entertain ourselves and use infrastructure that has been inspected and quality checked in some cases by AI – power lines, railroad tracks, flare stacks and maybe had the ocassional e-chat with a website bot making a general query about our utility, bank or insurance account…

The AI Now Institute is associated with New York University and is home to leading AI researchers from Google and Microsoft.   Their recent report points out vulnerabilities of the public as a result of the lag in regulatory protections that are unable to keep up with the rate of development in this space as its application becomes more widely used on social media tracking, facial recognition and emotional readings.  Schools are even looking to engage students via smart devices for educational purposes without considering the possible implications.

“Robots are tools to make our life easier, not replace real people” says Australian based PetScripts managing director and chief vet, Dr Mark Perissinotto. “Your veterinarian is absolutely the best person to examine your pet and advise on what medicines your pet needs, and Penny (the first chat-bot in the world specifically created to help pet owners buy pet medicines and subscriptions) is not intended to replace that.  But the process of getting scripts ordered and filled can and should be streamlined.   That’s where Penny and online pet pharmacies like PetScripts.com.au comes in.  Most first time online pet pharmacy customers need help or have questions and PetScripts wants to make getting that assistance as easy as we can. Because it’s simply not possible to have a real live Australian vet tech or pharmacy assistant available 24 hours a day, Penny is here to help”.

Doing more with less is the height of efficiency,  The main challenge which will be exciting to watch unfold will be the culture-bound constraints on data collection.  What’s most exciting will be the complex multidimensional weaving of data to provide in depth analysis on segments not possible for many before. 

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