Simulating human reasoning was the main reason Watson was introduced by IBM but now it has been broadened to include all other forms of AI. Much of the recent hype has been about machine learning that leads to predictive behavior and analysis for enterprises. Slowly, one of the most complex forms of AI, deep learning is also gaining momentum. The neurons in the human brains can connect to other neurons anyhow without any specific pattern. But neural networks using machine learning are a replication of the brain network and consist of more defined connections. Deep learning is a far more complex technology and addresses only elementary problems like text mining, language translation or image recognition.
Commercially it is still a difficult task to develop deep learning applications as compared to machine learning. The technology is worth a great deal that has elevated machine learning to a new level in terms of capabilities, say for instance sorting out large signal input problems in case of image classification for driverless cars or medicine-related, and document classification or speaker recognition. Within the next ten years, deep learning tools will become a standard component of every software kit.
A list of the future’s most sort after deep learning applications has been compiled.
1. Text Generation
A model will be generated by learning huge sets of text either word by word or character by character. This model will be able to grasp spellings, punctuations, sentence formations and neural networks will process the input signals and then generate text.
2. Voice Assistants
Nearly all smartphones now feature voice assistants. Tech giants like Google, Microsoft, and Apple have all come up with their voice assistants like Google Now, Cortana and Siri respectively. These assistants make use of natural language processing (NLP) to execute commands through voice input.
Converting a text from the original source to the target language might seem quite straightforward on the surface, but is actually quite complex. All the elements in the text need to be interpreted and analyzed which requires extensive grammar and semantics expertise, both in the source as well as the target language. Text and image translations are two areas where deep learning is seen achieving significant results.
4. Driverless Cars
Incorporating data from both external and internal sensors like radars, Lidar or cameras and IoT, driver conditions can be extensively evaluated. For this, companies train their algorithms using large datasets.
5. Finance and Neural Networks
The major fields of finance in which neural networks have been deployed are trading, business analytics, financial operations, product maintenance. In areas of risk assessment, neural networks are undisputed leaders. All kinds of traders can make use of these networks for forecasting and market research purposes. Price data can be thoroughly analyzed and opportunities can be uncovered by detecting not so obvious non-linear interdependencies or patterns which traditional analysis methods fail to do.
6. Earthquake Prediction
Viscoelastic computations are made use of in earthquake predictions. This method was developed by a team of Harvard scientists. Until this method had been discovered, the computations involved were very computer intensive. But this method improved the calculation time by 50,000% and time in case of earthquake prediction is a vital component.
These applications are already creating an impact and have made artificial intelligence smarter. The new wave of neural networks is expected to change businesses and experience on a whole new level.