Before the mid-2000s, few people outside of computer science and mathematics circles had heard of the word ‘algorithm’. Fast forward to today, and the term is a common part of day to day speech, and a hot topic that impacts everything from politics to consumer technology.
What few realize though, is that the notion of an algorithm dates all the way back to Islamic golden age, taking as it does its name from the influential Persian mathematician, Muhammad al-Khwarizmi. In devising a set of rules for solving equations, al-Khwarizmi gave rise to the very first algorithms.
Nowadays, we use algorithms to help us sort through the vast amount of data and information that we must contend with in the modern world. Algorithms can help us shop for items by suggesting products that complement things we’ve already got in our shopping basket, and they can help us figure out what to watch next on Netflix by taking into account our preferences and making predictions on what we may enjoy based upon the similarity of our viewing history to other customers.
Perhaps nowhere have algorithms made a bigger impact than in the realm of social media, where they have been employed to great effect to surface information, content, users and products that it predicts we will enjoy.
What is fascinating about algorithms is that they can grow ever more refined. It is for this reason that when you create a new account on a platform like TikTok, at first you are presented with a mixture of content – some of which you may find uninteresting.
But over time, as the algorithm records your responses and engagement with specific posts or videos, it will learn what you like and begin to show you more and more content that matches your interests.
While all of this has made navigating through the noise of data on the internet significantly easier, there are some specific use-cases where algorithms are not the best option for a business or developer to employ. Let’s take a look at some key examples of this below.
Lack of Human Input
One of the saving graces of algorithms is that they are a fantastic labor saving solution thanks to the fact that they effectively automate the process of recommending content to users. While that makes recommending a new movie to watch an ideal use-case for them, when it comes to other topics they can come up lacking.
Nowhere is this more obvious than when it comes to situations where people would naturally prefer the input of an expert who is better able to advise them and make bespoke selections on goods and services.
In some cases, it’s not uncommon for platforms that are perfectly capable of installing algorithmic solutions to instead defer to human recommendations. Take iGaming providers such as Casino Bonus CA as a clear example – this service, and others like it, have built their reputation on being able to provide custom and highly authoritative recommendations on the best sign-up bonuses for leading online casinos furnishing a specific region.
While an algorithm could theoretically achieve this aim, it would be deemed less reliable and would thus negatively impact the platform’s aims.
One criticism that is frequently directed at algorithms is that they can create walled gardens out of content. To illustrate what this means – imagine if you more-or-less only listened to K-Pop on Spotify. In this situation, Spotify would learn that you enjoy this music and focus on providing you with further K-Pop recommendations.
That’s all well and good, until one day you wake up and decide you want to listen to German classical music. Rather than relying on the algorithm to occasionally surface Beethoven or Bach, you would have to go out of your way to seek out this music yourself.
Dependence on Large Data Sets
Another curious aspect of algorithms that can result in them being less than ideal, is that to work well they typically require large data sets. Consider the Netflix example from above. If Netflix only had 3 users’ viewing history in its data-base, it would struggle to make recommendations based on this limited date.
Say one person is a fan of horror films, and almost exclusively watches movies and shows of this genre, and the other two users have little to no interest in them. In this situation, the horror fan would receive very little in the way of useful recommendations from Netflix’s algorithm as it has no other horror fans from which to draw its recommendations from.
- As per the Public Gambling Act of 1867, all Indian states, except Goa, Daman and Sikkim, prohibit gambling
- Land-based casinos are legalized, with certain guidelines, in Goa and Daman, as per the Goa, Daman and Diu Public Gambling Act 1976
- Land-based casinos, Online gambling and E-gaming (games of chance) are legalized in Sikkim under the Sikkim Online Gaming (Regulation) Rules 2009
- Only some Indian states have legalized online/regular lotteries as per and subject to the conditions laid down by state laws. Kindly refer to the same here
- Horse racing and betting on horse racing, including online betting, is permitted only in a licensed premise in select states. Kindly refer to the 1996 Judgement by the Supreme Court Of India here and for more information
- This article does not endorse or express the views of Qrius and/or any of its staff.
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