Latest Artificial Intelligence Trends for Enterprises

Artificial Intelligence has crossed several milestones.

Artificial intelligence is the most disruptive technology of our times, transforming business processes and the world we live in. Enterprises are using AI to extract the maximum potential and improve the overall customer experience. AI trends for 2021 are aligned in the direction of innovation. Achievements are already being seen in the form of algorithms. For example, Google's BRET transformer neural network is a new algorithm that promises to revolutionize NLP. In the same way, new tools are being developed for enterprises to automate machine learning tasks and accelerate innovation and solution development. AI is also advancing towards conceptual designs, smaller devices, and multi-model applications. Hence, it is important for tech organizations to be updated with what's available new in technology.

1. Automated Machine Learning

AutoML will bring in improved tools that will refine data labeling and neural net architectures. Labeling data is an essential component of the industries, which is now being outsourced to counties like India, Central Eastern Europe, and South America. Owing to the hectic shift in work brought by the Pandemic, companies are now looking for more 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 cheaply available and more new solutions can then be created.

2. AI-powered Conceptual Design

Traditionally, AI's major applications lie in streamlining processes related to data, image, and language analysis.  Industries including financial, retail, and healthcare used this technology to automate repetitive tasks. OpenAI's recent developments will change this mindset. Known as DALL E and CLIP, these models will combine language and images to generate new visual designs by understanding text descriptions.

3. Supporting Multiple Learning Areas

With frequent advancements, AI's ability to support multiple modes within a single ML model is improving. The technology can now leverage text, vision, speech, and IoT sensor data. Developers are also capitalizing on this ability and innovating various ways to better normal, mundane tasks. In the healthcare industry, patient data that is collected by healthcare systems include visual labs, clinical trial reports, and other documents. With the right layout style and presentation, it can help doctors understand the situation in a better way. AI models that can work with multi-modal techniques can do the job of presenting reports and improving medical diagnosis.

4. Miniature ML

Miniature or tiny machine learning is under development. These small models will run on devices that don't use much hardware, like microcontrollers for powering cars. Tiny ML algorithms can be used for local analysis of simple voice or gesture commands, to identify sounds like a gunshot or a baby crying.

5. AI for Employee Experience

Every time there's a new AI development, there's a fear that it will threaten human jobs. It leaders are combating this concern by augmenting employee experience with AI. AI will help take the burden off of humans, like in the sales and customer service teams. With RPA, AI can automate tedious tasks and free up human effort for better work.

6. Quantum Machine Learning

Quantum computing can make powerful AI and ML models. Tech giants like Microsoft, Amazon, and IBM have started to work on this technology, to make quantum computing more accessible via the cloud. With this potential, organizations can solve critical problems and look for quantum applications in each industry.

7. AI for Everyone

Regular developments in AI is making sure that AI is simplified for everyone to work with. Democratized AI will improve AI development by making it fast and accurate. Domain experts and others frontline workers will also be able to work with AI when the necessity arises.