Artificial Intelligence

Artificial Intelligence (AI) is no less than a magic tool that is successfully wheeling the world towards transformation and evolution. The high-end technology has tapped into every industry and sector imaginable, paving way for hyper-modernity and hyper-convenience. Artificial intelligence can not only accelerate revenue growth rates in businesses or diagnose complex medical conditions in patients but can also produce poetry and prose.

As the years passed, AI demonstrated wonders in different domains and disciplines. However, they say that nothing is perfect. Not even AI. Artificial intelligence is engulfed in several limitations. Critics have often pointed the shortcomings and drawbacks that AI entails.

Industry Wired invites you to take a deep dive into the areas where AI has failed to reach but there are promising efforts ongoing to overcome the challenges.

The In-twilight Zones Far Away from AI

The reason why it is important to learn about the grey areas, untouched by AI is because of a belief that “nothing is impossible for AI”. Addressing these challenges can help to shorten the inventory of AI limitations.

Use of Common Sense

Of all forms of human intelligence mimicked by AI, common sense is an exception. While AI solves the most complex problems, it fails to perceive the most simple things in its surroundings. For example, if Artificial Intelligence is asked about what a baby ate for lunch, it may fail to answer because of the failure in cognition. The need to wash hands or say prayers prior to eating is a shared common sense amongst humans that AI fails to grasp.

Common sense in humans originates from personality conditioning from childhood, experiences during growth years, and the ability to form mental representations of what we perceive. These are the factors that help us in remembering facts and dates. In case of deep neural networks, the limitation is that they cannot form mental pictures and hence they fail to resemble human intelligence through-and-through.

Learn Continuously and Adapt Spontaneously

An important point to note here is that AI does not learn, it is trained. AI does not adapt, it is deployed. During the process of training, AI models absorb an existing data set and performs tasks accordingly. The model is then deployed to generate insights. AI models responds only to the amount of information or data it ingests that marks its limitations in learning and adapting.

The Ability to Understand Cause and Effect

Machine learning and AI can identify subtle patterns and associations in data. However, Artificial Intelligence fails to showcase its abilities in understanding causal mechanisms and the patterns that underlie the real-world dynamics. One of the most pertinent examples can be when a machine learning model can detect the patterns of data fed to it but fails to establish if the crowing of a rooster causes sunrise.

Causal reasoning is an important part of human intelligence as it has been re-asserted by famous philosophers like Socrates and Plato. Causal reasoning is important for humans to make sense of arbitrary and interact with the surroundings we function within. However, the phenomenon is equally important for artificial intelligence. Unless and until AI is able to grasp the world in its truest form, the gaps in technology will be ever-persistent.

Ethical Reasoning

Humans function on logical, moral, and ethical reasoning. While Artificial Intelligence AI and ML models are adept in logical reasoning, it lags behind when it comes to ethical reasoning. AI-powered models are amoral which can sometimes give rise to social mayhems. A famous incident is of Tay, the bot by Microsoft. Tay praised Hitler and condemned feminists on social media that sparked outrages world-wide. Microsoft had to remove the bot from the internet immediately.