Artificial Intelligence imitates reasoning, learning, and perception of human intelligence towards the simple or complex tasks performed. Such intelligence is seen in industries like healthcare, finance, manufacturing, logistics, and many other sectors. But there is a thing common – mistakes while using the AI concepts. Making mistakes is quite generic and one can’t hide himself/herself from the consequences. So, instead of paying attention to its repercussions, we need to understand the reason why such mistakes may occur and then, modify the practices we usually perform in real-time scenarios.
Let’s spare some time in knowing about the mistakes we must be avoiding while getting started with learning Artificial Intelligence:
1. Starting Your AI Journey Directly with Deep Learning
Deep Learning is a subpart of Artificial Intelligence whose algorithms are inspired by the function, structure of our brain. Are you trying to link our brain’s structure and its functioning with neural networks? Yes, you can (in the context of AI) because there are neurons present in our brains that collect signals and split them into structures residing in the brain. This lets our brain understand what the task is and how it must be done. Now, you may try to begin your AI journey with Deep Learning (or DL) directly after knowing a bit about neural networks!!
No doubt there will be a lot of fun, but the point is that it’s better not to introduce DL initially because it fails to achieve higher performance while working with smaller datasets. Also, practicing DL isn’t only harder but expensive too, as the resources and computing power required for creating and monitoring DL algorithms are available at higher costs, thereby creating overheads while managing the expenses. Even at times when you try to begin interpreting the network designs and hyper-parameters involved with DL Algorithms, you feel like banging your heads because it is quite difficult to interpret the exact interpretation of the sequence of actions that a DL Algorithm wants to convey. All such challenges will come amidst the path of your AI journey and thus, it is beneficial not to introduce Deep Learning directly.
2. Making Use of an Influenced AI Model
An Influenced AI model will always be biased in an unfair manner as the data garnered by it will be inclined towards the existing prejudices of reality. Such an inclination won’t let the artificially intelligent algorithms identify the relevant features which reciprocate better analysis and decision-making for real-life scenarios. As a result, the datasets (trained or untrained) will map unfair patterns and never adopt egalitarian perspectives somewhere supporting fairness and loyalty in the decision-making process followed by AI-based systems.
To understand the negative impact of an influenced AI Model, we may take a look at the COMPAS case study. COMPAS is an AI-influenced tool whose full form is Correctional Offender Management Profiling for Alternative Sanctions. It is used by the US courts for predicting if or not the defendant may become a recidivist (criminal reoffending different sorts of crimes). When this tool examined the data, the results were really shocking. It predicted false recidivism by concluding that 45 percent of black defendants were recidivists, while 23 percent of white defendants were classified as recidivists. This case study questioned the overall accuracy of the AI model used by the tool and clearly describes how such bias invites race discrimination amongst the people of the United States. Henceforth, it is better not to use a biased AI model as it may worsen the current situation by creating an array of errors in the process of making impactful decisions.
3. Trying to Fit Accuracy of AI Algorithms with Every Biz. Domain
Every biz. (business) domain won’t try to fit accuracy in every of its ongoing or forthcoming AI processes either related to software development or customer service. This is because there are other traits business ventures consider, like robustness, flexibility, innovation, and so on. Still thinking what the reason could be!! The answer is – Accuracy is foremost, but interpretability has its own potential!
For instance, clients responsible for generating good revenue for business ventures check accuracy at a limit of say 90 percent, but they also check the robustness and flexibility of the AI algorithms while understanding the current business problem and then, predicting the outcomes much closer to their actual values. If the algorithms fail to factorize problems and do not realize the importance of data interpretation at times they are predicting the conclusions, clients straightaway reject such analysis. Here, what they are actually looking for is that AI algorithms are interpreting the input datasets well and showcasing robustness and flexibility in evaluating the decision-matrix suitably. Henceforth, you prefer not to fit accuracy with every domain generating visibility for businesses in the current or futuristic times.
4. Wasting Time in Mugging Up the AI Concepts
Mugging up the AI concepts won’t let you acquire a deeper understanding of the AI algorithms. This is because those theoretical concepts are bound to certain conditions and won’t reciprocate the same explanation in real-time situations. For example, when you enroll yourself for a course, say Data Science course, there are various terminologies embedded in the curriculum. But do they behave the same when applied to real-time scenarios?
Of course not! Their results vary because the terminologies when exposed to situations are affected by various factors whose results one can only understand after being involved in how these practical techniques fit well into a larger context and the way they work. So, if you keep mugging up the AI concepts, it would be difficult to remain connected with its practical meaning for a longer period. Consequently, solving the existing real-world problem will become challenging and this will negatively impact your decision-making process.
5. Trying to Snap Up all Swiftly
Snapping up swiftly here means hurrying up learning a maximum number of AI concepts practically and trying to create AI models (consisting of different characteristics) in a shorter span. Such a hurry won’t be advantageous. Rather, this will be forcing you to jump to conclusions without validating the current datasets modeled for understanding the business requirements well. Besides, such a strategy will be landing your minds in utter confusion and you will be having more problems, instead of solutions, in your pocket.
We may understand this through a real-life example. Suppose you are in the kitchen and preparing your meal. Now, your brother enters and asks you to prepare snacks within 20 minutes. Thinking if I am trapped or confused!! Yes, you will face endless confusion in deciding if you should be preparing your meal or the snacks for your brother. As a result, this will impact your accuracy of preparing quality meals/snacks because now, you have a time-boundation of 20 minutes. Such a situation occurs when one tries to snap up all the terminologies and notions embedded within an AI-based system/model. Therefore, instead of trying to grab everything quickly, you need to follow the SLOW AND STEADY principle. It will be helping you solve the existing AI challenge by selecting appropriately validated datasets not bound to non-accurate results.
