Interesting Artificial Intelligence Trends

New advances in artificial intelligence (AI) and machine learning (ML) research made by tech giants and academia have quickly made their way into businesses and business models, while even more companies are introducing established AI solutions like chatbots and virtual assistants. Following all that is happening in the dynamic world of AI is time-consuming for entrepreneurs who are busy running their own companies, so I’ve compiled a list of the most interesting AI trends entrepreneurs should keep an eye on in the coming year.

AI content creation
The trend toward humanization of big data and data analytics will continue in 2018 with new advancements in natural language generation (NLG) and natural language processing (NLP). Using rule-based systems like Wordsmith by Automated Insights, media outlets and companies can already turn structured data into intelligent narratives based on natural language.

Related: How Your Business Can Stay Ahead of the Game With Artificial Intelligence

Making relationships in data understandable to people beyond data science teams will further democratize AI and big data, leading to the era of automatic generation of insights. The same technologies are already enabling automated content generation in news coverage, social media, marketing, fantasy sports, financial reports and more. In the coming year, automated content generation is likely to gain more traction in news reporting and marketing, helping companies instantly respond to emerging trends, news and events by creating the relevant content for their audience and clients.

The rise of capsules AI
Capsule networks (CapsNet) is a new form of deep neural networks proposed by Google’s lead scientist Geoffrey Hinton in a recent paper. In a nutshell, a capsules approach aims to overcome the shortcomings of CNNs (convolutional neural networks) that have been the de facto standard in image recognition for many years. CNNs are good when images fed to them are similar to those used during training. However, if they are asked to recognize images that have rotation, tilt or some misplaced elements, CNNs have poor performance. CNNs’ inability to account for spatial relationships makes it also possible to fool them by changing a position of graphical elements or the angle of the picture.