AI News, Keras model summary explanation artificial intelligence

“AI Sells, Data Delivers!”

The world is advancing at a faster pace and new technologies are coming up to reshape the business world and society.

Apart from the context of the domain where AI is being applied, there are three main components of AI: The first one is the AI algorithm itself.Open-source machine learning libraries like Keras, Theano and TensorFlow have shed a lot of the low-level complexity involved in designing and building AI applications.

Source: While AI is a reasonably wide area of study in computer science,most of the excitement these days is centred on an area of AI called machine learning and in particular, deep learning.

However, unless you are Google or Facebook with vast amounts of representative data, you will struggle to harvest historical data that are enough to give the required inference for machine learning techniques to be an effective enabler for AI initiatives.

But with many businesses lacking the data infrastructure necessary to obtain real AI and ML capabilities, the journey towards perfect production can also be so abstract that it perplexes the very people looking to achieve it.

During the global launch of the Outside Insight book, author and Meltwater CEO Jorn Lyseggen, alongside AI experts, discussed the importance of the data fueling AI, and the need for executives using AI outputs for decision-making to both understand the data informing those outputs and ensure it’s as comprehensive and unbiased as possible.

Source: In the growing AI market, International Data Corporation (IDC) predicts global spending is expected to increase 50% per year, to a total of $57.6 billion by 2021.

Building a self-driving car requires a humongous amount of data ranging from signals from infrared sensors, images from digital cameras, and high-resolution maps.

While we have seen recent advances in other AI techniques like reinforcement learning that use less data (like the success of Deep Mind’s recent Alpha Go — Zero in the game of GO), data is still critical for developing AI applications.

Enterprises are overwhelmed with silo IT systems built over the years that contain data designed to do very specific individual ‘System of Record’ tasks, but unfortunately, these records are duplicated across multiple ‘Systems of Record’ resulting in massive data proliferation but lacking complete representation of an entity in any single system.

This reality has given rise to fragmented and often duplicated data landscape that requires expensive and often non-efficient means of establishing ‘Source of Truth’ data sets.

AI applications improve as they gain more experience (means more data) but present AI applications have an unhealthy infatuation with gaining this experience exclusively from Machine Learning techniques.

For example, if an AI model is learning to recognize chairs and has only seen standard dining chairs that have four legs, the model may believe that chairs are only defined by four legs.

Given that AI applications usually become more reliable the more they can correlate different sources of information, siloed data sets that are hard to access become an obstacle to discovering value in an organisation’s data.

One of the most significant things for AI to be successful is that executives and decision-makers have the data literacy to beat up the model, to challenge the model, to massage the model and to fully understand what the underlying assumptions are to make sure the answer it produces actually matches the terrain that you want to operate in.

For instance, if a model is trying to learn to recognize chairs and has only been shown standard dining chairs with four legs, it may learn that chairs are defined by having four legs.

This is where a developer takes a small amount of traffic to a site and tests to see whether a new recommendation engine or search algorithm performs better on that small set of traffic.

Recent work from Google has shown how training using data designated for a different task like image recognition can help performance on another completely different task like language translation.

From there, you’ll progress through increasingly advanced analytical capabilities, until you achieve that utopian aim of perfect production, where you have AI helping you make products as efficiently and reliably as possible.

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