5 predictions for the future of machine learning
There are countless articles and books on the future of machine learning. Today, we’ll keep the discussion down-to-earth with five near-term predictions:
Most applications will include machine learning.
In only a few years, machine learning will become part of nearly every software application. Engineers will even embed these capabilities directly into our devices. Think of how well your TV streaming service knows what to recommend. Expect this level of personalization to become ubiquitous and improve the customer experience everywhere.
Machine learning as a service will become more common.
As machine learning becomes increasingly valuable and the technology matures, more businesses will start using the cloud to offer machine learning as a service (MLaaS). This will allow a wider range of organizations to take advantage of machine learning without making large hardware investments or training their own algorithms.
Computers will get really good at talking like humans.
Before machine learning, computers had a very hard time understanding even simple human language. Machine learning helps computers understand the context and meaning of sentences much better though natural language processing (NLP). As the technology improves, solutions such as IBM Watson Assistant will learn to communicate seamlessly without using code.
Algorithms will constantly retrain.
Currently, most machine learning systems train only once. Based on that initial training, the system will then address any new data or problems. Over time, the training information often becomes dated or imperfect. In the near future, more machine learning systems will connect to the internet and continuously retrain on the most relevant information.
Specialized hardware will deliver performance breakthroughs.
Traditional CPUs alone have had limited success running machine learning systems. GPUs, however, have an advantage in running these algorithms because they have a large number of simple cores. AI experts are also using field-programmable gate arrays (FPGAs) for machine learning. At times, FPGAs can even outperform GPUs.
As specialized hardware continues to improve and become more affordable, more organizations will gain access to increasingly powerful machines. These improvements in the underlying hardware will enable breakthroughs in all areas of AI, including machine learning.
One trend is consistent across all five of these predictions: as this technology advances, more businesses will embrace the AI revolution. Competition to make the most effective use of data and machine learning will tighten. The teams with the strongest AI strategies will have a major competitive advantage.
To learn more, download your no-cost copy of “Machine Learning for Dummies.”
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