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Day: September 29, 2018

Does The Old Boys' Network Still Exist in Investment Banking

Posted on by Imarticus Learning

Imagine this – you are caught up in a minor disagreement or have a small task to do, to get your deal through. You immediately realise that you have some friends on the side of the other party, and you know the people since college. You only up, as any one of us would do, and he works on the deal, so it ends in the way you want it to – this has been how many of the largest settlements in investment banking have been done, traditionally.
Investment banking has always been thought to be dominated by an elite few. They constituted what was informally called an ‘old boys’ network’, and people who do not have the important contacts thought that they could never cut it in the competitive world of IB. In this article, we shall take a deeper look at what the Old Boys’ network exactly is, and whether it still exists in the Investment Banking world.

What is the Old Boys’ Network?

The term ‘Old Boys’ Network’ refers to an intimate connection or a network between some of the old students of a particular institution, or the early employees at an organisation. The term has its origins from the British Elite of the mid 20th century since most of them studied from a select few schools and colleges. The result was that the network helped the future students of the schools get employed by large corporations and banks, and the effect was passed on to the next generation. This is similar to an alumni association regarding how it works- however, it is less formal. The phrase ‘it’s not what you know, it’s whom you know’ is also applied here. An example of the Old boys’ society regarding organisations are the Paypal Mafia currently dominating the Silicon Valley, which is made up of the early employees of Paypal.
So does it still exist in the Investment Banking landscape?

Does the Old Boys’ Network still exist in Investment Banking?

The answer is that it does, but the effects have been significantly reduced. For example, the Investment Banking jobs in Britain used to be traditionally filled by the graduates from Oxbridge, until a couple of decades ago – this was as a result of their contacts, and the strong alumni network in place. However, organisations have now started hiring new employees based on their merits, rather than the institution they have graduated from.
The effect of the standardised recruitment process has been that talented students and employees from less prominent organisations have been getting the chance to show their skills at the job. The investment banking landscape has become increasingly decentralised, although some institutions are still held in high regard by recruiters – however, this is more because of the merits their students have, rather than the institution itself.
So if you worry that you do not have the right contacts in the IB world, you do not have to fret – if you have the skills and the passion, you are sure to get your job if you persist.

The Origins and History of Machine Learning

Posted on by Imarticus Learning

Before this article gets on to the details of machine learning, it will enlighten the reader about the definition and concepts of machine learning. Some of the researchers and studies suggest that machine learning is a technique of artificial intelligence where the system automatically learns from information and data.
IT professionals define machine learning as a process by which the machine automatically improves their performance and algorithms without getting programmed manually.  Thus it can be said that machine learning history has several benefits like publishing sports reports, driving cars automatically and improved interaction with humans with the help of algorithms.
Recent surveys have suggested that examining the machine learning history will give the industries overview that how machine learning works and how it is important to the evolution on a daily basis. The collection from the data presented below will make the reader clearly understand the origins of machine learning and how it has evolved with the present date.

History

Turning test (1950)

This is a test which named after Alan Turing. According to this test, he was in the constant search of stating the machine has its own intelligence. Thus in order to pass this test, the computer must able to convince that it is also a human operating system and operates with its own set of intelligence.

Computer learning program (1957)

Computer learning program is basically the first ever computer program written by Arthur Samuel. The program was designed in the form of a game known as checkers. The system would play this game and through its course would develop winning strategies of its own to win the game. This would prove that just like humans the machine also had artificial intelligence to make changes according to its own convenience.

Neutral network (1950)

The neutral network was first designed by Frank Rosenblatt. The neutral network came to be known as a perceptron. Now basically this was a network which works on human brains, meaning using this network stimulation of human thought can be analysed and processed.

Nearest Neighbor (1967)

Nearest Neighbor is basically a creative algorithm which was designed in order for the system to evaluate or recognize the pattern. For example, the pattern could involve a salesman’s route to a particular city ensuring that the salesman covers all the major cities that fall within the route

Stanford Cart (1979)

Stanford cart is basically a program which was designed by the students of Ford University to find out the obstacles in a living room. With the help of this program, the system would automatically find out the obstacles in the living room using its own artificial intelligence.

EBL (1981)

Earlier till the 70’s, machine learning was all about programming but 1981 saw a major change when Gerald Dejong introduced the world to explanation-based learning. Now explanation based learning or better known as EBL was training data analyses which would allow the system to capture the important documents and exclude the unimportant ones.

Net Talk (1985)

In 1985 Terry Sejnowski developed a program known as the Net talk. With the help of this program, the system would be able to pronounce words just as a baby does. The system will take note of the pronunciation of the baby and will automatically generate sound and words that would be an exact copy of the baby.

Data-driven approach (1990’s)

During the times of the 90’s, there came a time when machine learning moved from a knowledge-based approach to the data-based approach. With the help of this approach, the IT professionals could evaluate large chunks of data and draw conclusions from it.

ERA of 2000

The era of 2000 includes some major changes that took place in 2006. 2011, 2012, 2014. 2015 and 2016. As early as 2006 developed a program known as deep learning which would allow the system to analyze the videos, images, and text on its own. Come 2010, Microsoft was able to track human features giving the humans the ability to interact with the system in the form of movements and gestures.
By 2011 Google was able to identify the objects just as the cat does and by 2012 Google developed a program which shows specific results when a particular search was made in YouTube. For example, if the user searched for the dog then with the help of these programs only videos of dog will be shown. This program provided as a blessing to précised searching. In 2015 machine came in the form of machine learning online. Both Amazon and Microsoft were able to launch their own machine learning platform. Now with the help of machine learning online data could be available on multiple machines and now better prediction could be made future made which was possible due to the batch learning process.

Conclusion

Hence to conclude it can be said machine learning has faced various transformations over the years. Now it can be said that the computer basically have their own artificial intelligence where it can think and act on their own. This statement is contradicted by various researchers and scientist who say that a computer will think the same as a human brain does. This is like comparing apples with oranges. Though it cannot be unnoticed that transformation of machine learning growing at random space and there is no limitation to it. The main question arising here is that will the computers be able to grow continually as the data keeps getting larger and larger.