Data, Machine Learning, and AI in 2023 — 7 trends your business needs to know

Over the next few years, MAD (Machine Learning, Artificial Intelligence, and Data) has the potential to change the world, both in business and human terms.

PwC estimates that AI, for example, could impact the global economy by over $15 trillion by 2030.

Here are the top trends you need to have on your radar in 2023

1. Automated Machine Learning (AutoML)

On the horizon are improved tools for labelling data.

Data labelling allows AI and Machine Learning to build an accurate understanding of real-world environments. Up until now, organisations have had to have their data labelling completed manually, in low-cost offshore places like South America, India, and Central Eastern Europe.

New improvements in technology mean companies should, in future, be able to reduce the amount of data that’s labelled manually. New levels of automated labelling will bring down the cost of AI and allow companies to bring new solutions to market more quickly.

2. New Generative AI models

OpenAI recently developed two new AI models: DALL-E and CLIP.

CLIP can predict the most relevant text description for images after being given their text descriptions, while DALL-E is an AI system that can generate realistic images from natural language descriptions.

This could be a game-changer for implementing AI in creative industries such as fashion, architecture, and more.

3. Multimodal Learning

Google DeepMind recently made waves with Gato, a multimodal AI that can perform language, visual, and robotic movement tasks. Other developers are also looking into combining modalities to improve common tasks such as understanding documents.

Healthcare could benefit greatly from multi-modal learning, as AI algorithms, including Optical Character Recognition (OCR), could help present clinical results in a clearer manner leading to improved medical diagnosis and treatment.

4. AI for Multiple Objectives

AI is usually given one objective to fulfil — for example, to target a particular business metric like profit maximisation.

Moving forward, AI is expected to be able to carry out multiple tasks that consider multiple objectives. This will help companies develop models that balance environmental factors — for example, carbon reduction — with traditional business goals such as reduced delivery times.

5. AI and Cybersecurity

Cybersecurity will continue to be a priority and developers will need to find ways to use AI defensively and proactively to detect atypical behaviours and attack patterns before they cause problems.

6. AI — A mainstream phenomenon

AI has accelerated away from the realm of the technical to mainstream, with non-tech individuals now able to experience its power first-hand. More and more simplified AI tools are expected to drive adoption of AI even further outside of IT.

This will have far-reaching implications with potential impact on society, politics, and geopolitics. Organisations will have to carefully consider ethics and data privacy complications.

7. Removal of bias in Machine Learning (ML)

As AI accelerates, bias and fairness will become a bigger concern. The emphasis will be on ensuring AI makes accurate predictions and ensures people aren’t being discriminated against. In future there will be a need for more tools for monitoring and mitigating bias.

To be able to optimise the potential of AI, ML, and Data, companies will need talented Data Scientists with relevant skills, from Natural Language Processing to Machine Vision Techniques. This means investing in people who can bridge technical and business aspects and help them identify new opportunities.

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