More efficient machine learning could upend the AI paradigm

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Every year a new technology comes into our tech world. However, the way we see the advancement in the technology is how efficient the system has become through the years. Well, to estimate the quality of AI research we use more or less the same methodology. The shorter time it takes to train a particular AI system the more efficient it is. But what exactly involves in this process of making AI computations much easier and accessible?

AI systems work in ways very similar to our own brains

Usually in the scientific terminology we call them neural networks, which connect the different branches of learning to make the AI understand the entirety of the purpose of the program. A normal AI system takes numerous hours of computing, learning and processing. It needs thousands of examples from the well-defined datasets. To get accurate results, most of the AI dependent software’s collect real time information and process it simultaneously. However, this process is tedious and time consuming. More over the computational energy and costs are very high. When the pros and cons are correctly considered, the advantages surely over take the cons, but still to make the most out of it, we need something extra. That is reducing the time taken by the decision tree computations, using something called data pruning. Data pruning essentially figures out what parts of the decision tree does not contribute directly to the output and eliminates that section which reduces the burden on the computational unit.

When this particular scenario is considered, the chip manufacturing companies are running towards increasing the power of processing to support the needs of AI systems. If the algorithm length is reduced the high processing chips won’t make any difference because of the cost calculations. To amount spent on the chips can be reduced by making the algorithms efficient. This is certainly plausible but not realizable at this moment.

Many researchers are putting tremendous efforts in making this very theory plausible.

Very recently google has announced an AI platform called Cloud AutoML, which simplifies the process of training the AI systems. Using their already built platform users can easily customize according to the current needs. To achieve this Google has spent significant amount of computing and time. This can not be done everywhere. As said earlier, Data pruning or techniques similar to that are very efficient when compared to this. Achieving something like this is utterly difficult and consumes enormous amount of human efforts.

The chip manufacturing companies like Intel, Qualcomm and Nividia are striving to make their chips faster while also increasing the price for the same. The question still remains, the efficiency in the AI platforms can really over throw the need for the expensive and fast processors but to what extent? Finally, this can totally upend the entire AI architecture in ways never dreamt before. The advancement of the technology was always dependent on how fast users adapt to it, and according to the industry experts, this will hold true even for efficient machine learning supported AI systems.

 

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