Artificial intelligence is the most disruptive technology of our time, transforming business processes and the world we live in. Companies are using artificial intelligence to extract its full potential and improve the overall customer experience. Artificial intelligence trends for 2021 are moving in the direction of innovation. The results are already visible in the form of algorithms. For example, Google’s BRET transformative neural network is a new algorithm that promises to revolutionize NLP. Similarly, new tools are being developed for businesses to automate machine learning tasks and accelerate innovation and solution development. Artificial intelligence is also moving towards conceptual models, smaller devices and multi-model applications. It is therefore important for technology organizations to keep abreast of available technological developments.
1.Machine learning
AutoML will provide enhanced tools to improve data labelling and neural network architectures. Data tagging is an essential component of industries, which is now outsourced to countries such as India, Central and Eastern Europe and South America. Because of the pandemic-related business disruption, companies are now looking for extra days to avoid or minimize this part of the process. The advantage of automating the work of selecting and tuning a neural network model is that AI will become available at a low cost and therefore more new solutions can be created.
- Conceptual design using AI
Traditionally, the main applications of artificial intelligence have been in optimizing processes related to data, image and language analysis. Sectors such as finance, retail and healthcare have used the technology to automate repetitive tasks. Recent developments in OpenAI will change this mindset. Known as DALL E and CLIP, these models will combine language and images to generate new visual models by understanding textual descriptions. - Supporting multiple learning domains
Due to frequent advances, the ability of artificial intelligence to support multiple modalities within a single ML model is improving. The technology can now leverage data from text, vision, speech and IoT sensors. Developers are also taking advantage of this capability and innovating in various ways to improve common tasks. In healthcare, patient data collected by health systems includes visual labs, clinical trial reports and other documents. With the right layout and presentation style, this can help doctors better understand the situation. Artificial intelligence models that can work with multimodal techniques can do the job of presenting reports and improving medical diagnosis. - Miniature ML
Miniature or tiny machine learning is being developed. These tiny models will work on devices that don’t use much hardware, such as microcontrollers to power machines. Tiny ML algorithms can be used for local analysis of voice commands or simple gestures to identify sounds such as a gunshot or a baby crying. - AI for employee experience
Every time a new development in artificial intelligence emerges, there is concern that it will threaten people’s jobs. Leaders combat this concern by improving the employee experience with artificial intelligence. Artificial intelligence will help offload human tasks, for example in sales and customer service teams. With RPA, artificial intelligence can automate tedious tasks and free up human effort for better work. - Quantum machine learning
Quantum computing can create powerful AI and ML models. Tech giants such as Microsoft, Amazon and IBM have started working on this technology to make quantum computing more accessible via the cloud. With this potential, organizations can solve critical problems and pursue quantum applications across industries. - AI for all
The constant evolution of artificial intelligence is making things easier for everyone. Democratized AI will improve AI development, making it faster and more accurate. Industry experts and other frontline workers will also be able to work with AI when needed.
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