One of Deloitte’s latest reports reveals that the number of machine learning pilot projects will double in 2018.
The artificial intelligence it is able to predict the success of a startup or the risk of Alzheimer’s thanks to tools such as machine learning or machine learning. Machine learning is the only area that works within AI and allows us to study large volumes of data that, otherwise, our brain would be unable to analyze correctly.
This technology works with algorithms capable of analyzing this information in multiple dimensions, drawing a common pattern and making predictions based on data, not intuitions. Its progress is of such magnitude that most large companies have already realized its benefits and, according to recent Deloitte research, corporations will double the use of machine learning by 2018.
According to the report “Technology, Media and Telecommunications Predictions“from 2018, the number of implementations and pilot projects that will use this technology over the next year will double compared to 2017, and by 2020 they will double again. In fact, IDC forecasts that AI and ML spending will increase from $ 12 billion in 2017 to $ 57.6 billion by 2021, although the adoption of machine learning is still in its early stages.
Factors for machine learning development
Deloitte has identified in its analysis five factors that have slowed the growth of machine learning: too few practitioners, high costs, too young tools, confusing models and trade regulations. In short, these advances for ML progress have to do with automation, data reduction and training acceleration, that is, making this technology easier, cheaper and faster to expand your market.
The ML market continues to improve, and it is likely, as Deloitte indicates, that another key improvement will emerge during 2018. In detail, the five factors that the company points out to not slow progress are:
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Automate data science so that the tasks of ML and information exploration do not take up so much time and space.
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Reduce the need for training data, since training an ML model can require up to millions of data and thus reduce time and cost.
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Speed up such training, the required calculations and the transfer of information.
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Explain the results with confidence in the responses generated by an ML model.
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Local deployment. The use of ML will grow along with the ability to deploy it where needed. Applications of such technology need to be expanded to smart homes and cities, autonomous vehicles, and portable and industrial technology.
Other emerging technologies
The report also highlights the use of other emerging technologies, such as the augmented reality. In total, 1,000 million smartphones will develop content in augmented reality, at least once during 2018. For example, in Snapchat or Instagram animated filters. Augmented reality will merge with the camera-based applications of our smartphone.
On the other hand, Deloitte predicts that, by the end of 2023, smartphone penetration in developed countries will exceed 90%%; and sales for the same year will be close to 1.85 billion. The secret of the smartphone’s success for the next five years will be the introduction of innovations that will make it easier for users to use, such as depth map-based recognition or enhanced functionality.
The advancement of new technologies such as machine learningg is unstoppable, and in the coming years we will see its development implemented.
