Transfer learning, cross lingual word embeddings and green AI: IntraFind explains three key AI trends for the coming year.
Franz Kögl: Director of IntraFind, the specialist for enterprise search and content analytics.
Artificial Intelligence (AI) will again make its mark on the IT world in 2021. IntraFind, specialist in enterprise search and AI, highlights three trends for text-focused AI and natural language processing that play an important role in this.
1. Transfer Learning. After 2020, transfer learning will remain a central AI trend this year as well. Transfer learning is a special method of machine learning and enables neural networks that have already been pre-trained for a specific purpose to be used as a starting point for another task. What has already been learned from a trained network can thus 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. Transfer learning will advance the democratization of AI and accelerate its widespread use in the corporate world. So far, AI has only worked particularly well in companies if they have a lot of data – which is rarely the case in practice – and use tailor-made models.
2. Cross Lingual Word Embeddings. Numerous applications of Natural Language Processing (NLP) are only available for the most important European 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. These CLWEs take advantage of the fact that many languages have semantic similarities. They capture these similarities and can represent words in multiple languages in a common vector space.
3. Green AI. Artificial intelligence penetrates more and more areas of life and business. The ecological footprint that it leaves when training algorithms and using them is growing accordingly. Against the background of increasing awareness of environmental protection and climate change, green AI is therefore becoming increasingly important. 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 generation, the operation of the network infrastructure and energy use as efficient as possible.
“In the next year, the democratization of AI will continue to pick up speed. Transfer learning can simplify many office activities without having to collect large amounts of data and develop customized models for each one of them. We have optimized our products to precisely match operational reality, so that every employee can ultimately improve their performance even without AI experts with very little expenditure of time for training the AI processes and thus help the company to save costs and improve its competitiveness, “says Franz Kögl, CEO of IntraFind Software AG . “But the topic of sustainability will also gain momentum.”
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