Recent advances in machine learning have been nothing short of amazing. Tasks that appeared impossible only a few years ago are now almost commonplace:
Now is the time for your business to start planning for that future. In this article, you will discover how machine learning can help your business grow.
There exist some awesome techniques:
= The addition of sounds to silent video
= Automatic handwriting recognition and generation
= Automatic language translation
= Classification of objects in photographs
= Colourisation of black and white images
However, in my experience, the one thing businesses have in abundance is text. It is everywhere! Be it in process documentation, sales literature or customer feedback – the list is endless. Luckily, we now have proven techniques to build infrastructure and develop algorithms that bring natural language processing (NLP) to the enterprise.
Driven by recent breakthroughs in deep learning research, machines can now understand and analyse human language better than ever before. Building on these advances, we can create technology and products to help businesses extract value from data.
Machines are exceptional at classifying text, extracting structured data, translating between languages and having meaningful conversations with customers. The business applications of such advances are limitless. By automating mundane tasks currently done by humans, we can free our workforce to focus on activities that add greater value.
Here are some examples to get you started:
Use sentiment analysis to detect positive or negative feelings in text. Monitor what customers are saying about your products on social media, or prioritise angry customer service inquiries. Until recently, sentiment analysis techniques were based on dictionary methods that could not deal with negation or information across sentences. We can now predict sentiment based on the true meaning of text, leading to much better accuracy.
Allow customers to deal with your company through conversational chat interfaces, or chatbots. For example, a chatbot could handle customer service inquiries, fulfil orders or make product recommendations.
Useful information is often hidden in unstructured text, but most software can only analyse structured data. Use NLP techniques to automatically extract facts from unstructured text, build a database or knowledge graph and feed the resulting structured data into standard applications like spreadsheets or data visualisation tools.
Building machine translation systems used to take years of engineering effort and a combination of sophisticated machine learning techniques. Using the vast amount of multilingual data available today, we can now train a neural network to translate automatically between various languages, saving cost, reducing engineering effort, while improving performance and accuracy at the same time.
Classify reviews, customer service conversations, news articles, sales emails and other types of text into useful categories based on the meaning of the text.
Find the most relevant text passages, advertisements or other items for a given user query or question. Instead of relying on keyword matching and word frequency measures, we can now score the semantic relatedness of documents, find relevant items, even if they do not contain exact matches, and build more intelligent search engines.
Contact us if you would like to learn more about using AI and machine learning in your business.
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