Garry Kasparov wrote, “Playing chess, I learned the dramatic effect combining humans and machines. Humans have intuition, can recognise patterns and positions, and machines have brute-force of calculation and memory. By bringing these capabilities together in other walks of life, we can achieve incredible results.” One of these results is () in M&A due diligence.
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Kasparov has credibility, or perhaps notoriety, in this regard. In his “Waking Up” podcast, philosopher and neuroscientist Sam Harris reminded Kasparov, “You will go down in history as the first person to be beaten by a machine in an intellectual pursuit where you were the most advanced member of our species.” 1 Kasparov points out that he actually beat IBM’s Deep Blue in 1996, though he lost to it a year later. Kasparov wanted a so-called “rubber match” to decide the matter forever, which did not happen. Just like in M&A: once the deal is completed, a rematch is nearly impossible.
Although not quite synonymous, the terms and cognitive computing are used interchangeably herein if other authors did so. By either term, we are considering the following: “Modeled after human , smart machines process massive data, identifying patterns. These patterns are used to ‘create’ entirely new patterns, allowing machines to test hypotheses and find solutions unknown to the original programmers.”
Getting the hang of data
Voluminous due diligence was not always so influential in dealmaking. “The art and science of merger execution have made great strides since the late 1990s — a period when stock-market frenzy often led to a rush to judgment, and ultimately to buyer’s remorse. Since then, a more prudent, systematic approach to mergers and acquisitions has emerged,” and there is much discussion of strategic due diligence.
Add to the mix the volumes of correspondence data and other data generated within corporations. It can be said that is a remedy to the build-up of this data. It’s a computerized response to a computerized problem. “ is an overarching term that includes many branches and sub-sets of technologies. However, the most common forms of in the legal tech sector are Machine , Deep , and Natural Language Processing.” […]