The Third Eye: Moving from Information Age to ‘Age of Intelligence'
This shift is compelled by the reality that there was no competitive gain from having information that everybody else also had and that it is the ownership of 'exclusive knowledge' called Intelligence that gave one advantage over the others.
AI applications are becoming a means of generating and accessing such knowledge largely through Data Analytics. Any information of intelligence value has to be 'reliable' but also 'futuristic' in the sense that it indicates the 'opportunities' and 'risks' lying ahead and thus opens the pathway to gainful action. To the extent a system of algorithms can be put in place to produce 'insights' during the analysis of data, this came closer to bridging the gap between 'Artificial' and 'Human' intelligence. Fundamentally, however, AI was an 'assistant' for and not a 'substitute' for human intelligence.
Someone rightly said that Artificial Intelligence backed by Large Language Models(LLMs) can become the ultimate repository of human knowledge but even when it might be able to decide what was 'factually true or false' it could not take the stewardship of determining what is 'right or wrong'. This can only be done by the human mind that is equipped with 'intuition' rooted in conscience, piety and a capacity to think for the future.
The power of logic -another singular feature of the human mind- derives from a combination of past experience, the capacity to observe and analyse information and the ability to see things in a 'cause and effect' mode. To a limited extent 'logic' can be built into the 'machine learning' but only in a borrowed way.
Moreover, human conduct is often conditioned by the 'system of moral values' followed at the personal level- biases and wishful thinking are often built into any system of morality- and this is yet another area where Artificial Intelligence would not be able to substitute the human mind.
AI essentially works on data in the memory and the Language Models enhance its outreach to demographies and customs bringing it somewhat closer to human behaviour but what stands out in all of this is the fact that AI cannot be freed from the 'input-output' principle.
Albert Einstein famously said that 'imagination was more important than knowledge'- he was not referring to the trait of wildly imagining things that some people might have but was defining the human capacity to see beyond the data in front and perceive what lay ahead. In a way, he was alluding to the ability of the human mind not to 'miss the wood for the tree'.
Imagination and human feedback are great assets in both business and personal lives and they mark out human intelligence from the machine-led operation. They both are of great help in the areas of Customer Relations Management- since they made it possible to personalise this relationship -as well as Risk Assessment which no successful enterprise could do without.
It is important to know the difference between 'intelligence' that tests the reach of the human mind and 'machine learning' that has its own boundaries.
Intelligence by definition is information that gives you an indication of 'what lies ahead' -Artificial Intelligence is therefore going to buy its importance from its capacity to produce 'predictive' readings.
AI has the limitation of being able to only read 'patterns' in the data examined by it and if the data was about the footprints left behind in the public domain by the 'adversary' or the 'competitor', this could enable data analytics to throw light at least on the 'modus operandi' of the opponent and indicate how the latter would possibly move next. There is a partial application of 'logic' here though not of 'imagination' which was an exclusive trait of the human mind.
If AI cannot be a substitute for human intelligence the best use of it is in making it an 'assistant' for the latter and this is precisely what explains the phenomenal advancement of AI in professional and business fields. A 'symbiotic relationship' between the two guarantees a bright future for humanity at large. Data analytics can aim at bringing out trends relating to the business environment, the study of the competitors and the organisation's internal situation. It can focus on the examination of the specific requirements of a particular business, organisational entity and profession, in a bid to seek a legitimate competitive advantage.
AI is strengthening the 'knowledge economy' by helping to evolve new services and products, by making things more efficient through cost-cutting and optimal utilisation of the available workforce and by generally improving the 'quality of life' by encouraging innovation. The constant change of the business scene because of the shifting paradigms of knowledge, establishes that any AI application will not be a one-time event and will further advance the cause of research and development. The determination of the 'direction' of an AI operation, however, will remain with the human mind and this placed a fundamental limitation on AI.
As the field of Al gets enlarged, two things are emerging as major concerns- the challenge of establishing the reliability of the data banks used and the likely use of AI for unethical and criminal objectives. In the age of fake news and misinformation on social media, only verified information must be used for AI applications. Confirming the reliability of data is by itself a task for AI that would create value for business.
India as a matter of policy favours international oversight of AI research in the interest of transparency for safeguarding the general good. The US thinks of AI development purely as an economic instrument and wants to preserve ownership rights in research and innovation.
At the strategic level, AI has the potential for providing new tools of security and intelligence and in the process, can become a source of threat for the geopolitical stability of the world itself. India has rightly taken the lead in demanding the ethical advancement of AI for the good of humanity and called for a collective approach to minimising the 'perils' of AI while promoting its progress for universal causes.
It is instructive to note that the recent joint winners of the Nobel Prize for Physics- John J Hopfield of Princeton University and Geoffrey E Hinton of the University of Toronto- are pioneers in the field of modern 'machine learning' research and they have both warned that AI had the potential to cause 'apocalypse' for humanity.
(The writer is a former Director Intelligence Bureau)
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