Top Deep learning trends behind AI development

By Srikanth
6 Min Read
Top Deep learning trends behind AI development

Technological innovations in artificial intelligence are transforming the way business is conducted and society as a whole. Considering AI trends in 2021, what should enterprises keep an eye on? Algorithms and their evolution frequently appear in success stories. A new type of algorithm that can revolutionize natural language processing is Google’s BERT transformer neural network. Also impressive and worth the business’s attention are the recent advancements in machine learning pipelines. They simplify the development process and vastly accelerate it. 


Further, AI is expanding into many new fields, including conceptual design, small devices, and multimodal applications that will enable AI to flourish in many industries. The most promising AI technologies are available via the cloud today, and quantum AI is one example. Companies should also pay attention to cutting-edge AI technologies that have incredible promise. So, what are the top deep learning trends behind AI development in 2021? Read to know. 

Business and IT leaders must devise a strategy for aligning AI and machine learning trends with employee interests and goals to get the most out of AI. On the agenda should be the following topics:

  • Automating and democratizing AI access;
  • Concerns about responsible and ethical AI; and
  • the need to align AI compensation with business objectives to ensure AI solutions achieve their goals.

We have compiled a list of top IT trends that leaders need to be prepared for by now. Read ahead.

AutoML – Automated machine learning

Automated machine learning’s most promising features include enhanced tools for labeling data and the automatic adaptation of neural net architectures.

  • With increasing requirements for labeled data, there was a human annotation industry based in low-cost nations like Central Eastern Europe, India, and South America. The risks associated with outsourcing have “led to the market exploring ways to avoid or minimize the associated risks.” As companies improve semi- and self-supervised learning, they can reduce the need to label data manually.
  • AI will become cheaper and less time-consuming if automated on selected tuning neural networks.

Gartner predicts that the future will revolve around three main operational processes: MLOps, PlatformOps, and DataOps. Gartner calls these capabilities XOps.

Conceptual design aided by AI

Data, image, and linguistic analytics have been the main applications of AI in the past. It is ideal for repetitive tasks in the financial, retail, and healthcare industries. Recently, OpenAI has developed new predictive models called DALL*E and CLIP that creates new visuals from text descriptions using language and images.

Models adapt to create novel designs using early work. For instance, AI has designed an avocado-shaped armchair just by the instruction in the caption as “avocado armchair.” The new models will facilitate the implementation of AI on a mass scale within the creative industries. Fashion, architecture, and other imaginative fields will see an impact of something similar.

Multimodal learning

Multimodal computer vision, text mining, speech analysis, and IoT sensor data are supported more seamlessly within a single machine learning model. There is a growing trend among developers to combine multiple modalities for routine tasks like document understanding. A healthcare system can collect and process patient data such as genetic tests, visual lab results, and clinical trial forms. 

In the systematic format and presentation, this information can help with observations for medical professionals. A medical diagnosis can be made more precise with the aid of multimodal AI algorithms, such as machine vision and OCR. To maximize the benefits of the multimodel approach, you require a mix of cross-domain skills, such as machine vision and natural language processing.

Tiny ML

A recent development in AI and Machine Learning is tiny ML. It runs on low-power devices such as microcontrollers that power refrigerators, cars, and utility meters. A limited set of ML algorithms help to analyze voices and gestures. For instance, a localized analysis to identify gunshots or baby tears. To verify or locate assets and determine their orientations, Or analyze any vital signs. Developing, securing, and managing Tiny ML will require new approaches.

AI-enabled employee experience

Concerns are growing about the possibility of AI stealing or dehumanizing jobs among IT leaders. It is for this reason that AI is gaining popularity for enhancing the employee experience. Artificial intelligence can be helpful to departments that are having a hard time hiring people, like in the sales and customer service sectors. 

Automation of mundane tasks using AI can help sales teams spend more time with customers. With a substantial chunk of work off-loaded from them, they can concentrate on meaningful duties. In addition, it could aid better in training and coaching employees.

While the change in trends is constant and overwhelming, understanding the needs of your business can equip you better for the challenges of the industry. Look out for what fetches a difference and navigates your business through the problems. All that as you remain a productive business entity. So, why wait? Which of the above trend is a valuable resource to your firm?

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