“Lets take a close look at three related terms (Deep Learning vs Machine Learning vs Pattern Recognition), and see how they relate to some of the hottest tech-themes in 2015 (namely Robotics and Artificial Intelligence). In our short journey through jargon, you should acquire a better understanding of how computer vision fits in, as well as gain an intuitive feel for how the machine learning zeitgeist has slowly evolved over time.”
1. Pattern Recognition: The birth of smart programs
Pattern recognition was a term popular in the 70s and 80s. The emphasis was on getting a computer program to do something â€œsmartâ€ like recognize the character “3”. And it really took a lot of cleverness and intuition to build such a program. Just think of “3” vs “B” and “3” vs “8”. Back in the day, it didnâ€™t really matter how you did it as long as there was no human-in-a-box pretending to be a machine.
2. Machine Learning: Smart programs can learn from examples
Sometime in the early 90s people started realizing that a more powerful way to build pattern recognition algorithms is to replace an expert (who probably knows way too much about pixels) with data (which can be mined from cheap laborers). So you collect a bunch of face images and non-face images, choose an algorithm, and wait for the computations to finish. This is the spirit of machine learning. “Machine Learning” emphasizes that the computer program (or machine) must do some work after it is given data.
3. Deep Learning: one architecture to rule them all
Fast forward to today and what weâ€™re seeing is a large interest in something called Deep Learning. The most popular kinds of Deep Learning models, as they are using in large scale image recognition tasks, are known as Convolutional Neural Nets, or simply ConvNets.
Machine Learning is here to stay. Don’t think about it as Pattern Recognition vs Machine Learning vs Deep Learning, just realize that each term emphasizes something a little bit different.
“But what can get lost in the eye-popping statistics around excess email and meetings is this: Collaboration overload is almost always a symptom of some deeper organizational pathology and rarely an ailment that can be treated effectively on its own. Attempts to liberate unproductive time by employing new tools (for example, Microsoft Teams, Slack, Box) or imposing new guidelines and meeting disciplines will prove fruitless unless steps are taken to deal with the underlying organizational illness. Companies that have successfully combatted the excesses of overload have done so by focusing on the root causes of unproductive collaborationâ€”and not merely the symptomsâ€”in devising the cure.”
Simplify the operating model. A companyâ€™s operating model encompasses its structure, governance, accountabilities, and ways of working.
Align the organization. Even when an organizationâ€™s structure is lean by most accounts, it can be misaligned. As a result, it may take more interactions than it should to get work done.
Set a zero-based time budget. One discipline that we have seen work to reduce the number of unnecessary meetings is to create a fixed meeting time bank in which all new meetings are funded out of the current bank.
Require business cases for new initiatives. When a company makes a major capital investment, senior management nearly always demands some form of business caseâ€”that is, an explicit statement of the expected benefits from making the investment weighed against the costs.
Provide real-time feedback. In some instances, it is possible to modify an organizationâ€™s cultural norms by providing its leaders with real-time data on the load they are placing on their teams as a result of the emails they send and meetings they schedule.
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