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With 2023 now in full swing, we are in the midst of a red-hot AI summer. It can be a job in itself, keeping up with all the developments within AI, while ignoring distractions. Here were summarize some of the AI trends to watch out for in 2023, so you can keep up with current developments.

There are many articles out there which provide a high-level overview of statistics and machine learning for non-technical executives. However, these are often so high-level that they don’t provide a good understanding of the underlying process and mathematics or require a background in programming and mathematics which the reader may not have. In this article, we take a detailed look under the hood at exactly what is being done when we train a Machine Learning model, without the need for a programming or strong mathematics background.

For a long time, people have not really thought of computers as creative, or original. To automate mechanical and repetitive tasks, sure, computers are fantastic, but for inspiration and creativity, surely this was the sole domain of humans? Over the past few years there have been huge advances in artificial intelligence for the purpose of content generation, and this assumption that machines cannot innovate or create, is rapidly eroding.

In the past the way companies and consumers was simple, slow, and predictable. Brands would research their market through traditional surveys and focus groups. Once a new product had been developed, brands would advertise through traditional media such as TV, radio, print, billboards, and we, the consumer, would go out and buy them. A lot has changed in the past couple of decades. Consumers now research products in an instant via search engines, talk openly about the brands and product they like or dislike on social media and leave feedback immediately in the form of reviews on eCommerce sites. This means there is a huge swathe of data companies can use to better understand the digital consumer instantly. There are advantages in this new world, in that customer feedback is faster, companies can identify consumer trends quicker and create products and services to suit their needs. However, without the right tools and techniques, this mass of unstructured data can easily turn into a cacophony, which is difficult to decipher and derive useful insight for brands. Much of this consumer data, whether that be customer reviews, social media posts or search engine queries is in the form of natural language. Natural language processing (NLP) is a range of techniques for analysing and representing naturally occurring text.

The top consumer goods companies are starting to use artificial intelligence to optimise their operations, increase sales and improve the customer experience, find out how here.

Sectors that are seeing a huge impact from artificial intelligence are the Consumer Goods & Retail industries. Retail companies that were born in the digital era, such as Amazon and Alibaba, have leveraged machine learning across all their eCommerce platforms, whereas traditional retailers are playing catch-up. Consumer goods companies which often have less of their own first-party data, as they often don’t sell directly to consumers like retailers, rely on third-party data, including social data, search data and product review data. Artificial intelligence is helping consumer goods companies make sense of this unstructured data with natural language processing. In this article we look at some of the top use-cases for artificial intelligence/machine learning in the Consumer Goods& Retail industries, and how to identify use-cases within an organisation.

Companies are investing an ever-growing amount of time and money into data science and artificial intelligence. In order to make the most of this investment, it is worth structuring the organisation and establishing the ways of working to make sure data science teams...

Here, we explore the components for a modern data governance program. While they may require an organisational change to achieve, in the long run, it will allow for Enterprise AI at a scale that is responsible and sustainable.

If “Data Scientist is the sexiest job in the 21st Century”, then data quality is the least sexy aspect, but that doesn’t take away from its critical importance.