Artificial intelligence is and will remain a hot topic and continue influencing the IT world. IntraFind, a specialist in AI, highlights three trends for text-focused AI and natural language processing (NLP) that will play an important role.
Transfer learning is a special method of machine learning. It enables neural networks that have already been pre-trained for a specific purpose to be used as a starting point for another task. In other words, what has already been learned from a trained network can be used for a new project. With this method, training a neural network is significantly less computationally intensive and time-consuming, and the amount of training data required is significantly reduced. Definitely, transfer learning will advance the democratization of AI and accelerate its widespread use in the corporate world.
Cross Lingual Word Embeddings
Numerous Natural Language Processing (NLP) applications are only available for the most important languages - often even exclusively for English. An important reason for this is that the expansion of NLP models to new languages usually requires the time-consuming annotation of completely new data records and is very computationally intensive. For lesser-used languages, however, there is often not enough training data available. Multilingual models with so-called Cross Lingual Word Embeddings (CLWEs) can help here. These CLWEs take advantage of the fact that many languages have semantic similarities: they grasp these similarities and can represent words in several languages in a common vector space.
Artificial intelligence penetrates more and more areas of life and business – leaving an ecological footprint when training algorithms and tendency to rise due to growing use. Against the background of increasing awareness of environmental protection and climate change, green AI is therefore getting more attention. For example, there is increasing research into algorithms that require less energy, less memory, and less communication bandwidth. The energy supply and efficiency of the data centers used for AI are also being scrutinized more and more often. Another important aspect of green AI is the use of algorithms to make energy production, the operation of the network infrastructure and energy use as efficient as possible.
“The democratization of AI will continue to gain momentum. Transfer learning can simplify many office activities without having to collect large amounts of data and develop customized models for each of them. We have optimized our products to reflect operational reality so that every employee can ultimately improve his/her performance with very little expenditure of time for training the AI processes and thus help the company to save costs and increase its competitiveness", Franz Kögl, CEO of IntraFind Software AG, states.