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AI, Data Science and Machine Learning

Latest Trends In AI And How They Can Help Your Business

Artificial Intelligence (AI) has become a main stay across the industries and right from CXOs to developers, all are looking to leverage AI and Data Science in a way that it will become a standard building component of various business processes. AI can bring in both bottom line and top line impact for the organizations with comparatively less investment costs. Incorporating AI and Machine Learning will be a paradigm shift for the companies who have till date depended on rule-based engines to drive their businesses. This helps to establish an autonomous and a self-healing process which will evolve over time to become an indispensable part of an organization growth.

Latest Developments

Literally, every month we evidence new developments and trends happening in the fields of AI and Machine Learning. That often leads to applications that radically transform businesses. Here are the hottest ones.

Artificially generating images using Generative Adversarial Networks

Generative Adversarial Networks (GANs) are relatively new Deep Learning models that allow to generate artificial instances (mostly images) based on instances presented before. There are various versions of GAN models such as StyleGAN, DCGAN, etc. allowing to transfer style, motifs, etc. This, in turn, finds many applications for creating artificial images of art works, design patterns, models, etc.

Business Implication:

  • Garment, Art and Texture Design (brings novel design ideas, reduces costs)
  • Financial modelling (generates synthetic data for training complex models)
  • Other: noise reduction in images, etc.

Deep Learning Frameworks for Edge Devices

With proliferation of Edge Devices, there is strong necessity to deploy deep learning models which can utilize limited resources and computational power, however, provide the desired output. Traditional Deep Learning models which require GPU power cannot be deployed in such devices and therefore require a converter to compress these models without major loss on performance. Deep Learning frameworks such as TensorFlow and PyTorch have recently released such converters.

Business Implication:

  • Advanced analytics on Edge Devices e.g. complex computer vision tasks on Smartphones (face recognition, pose detection, etc.) and cameras (personal Identification)
  • Other: wearable devices analytics, etc.

Large Scale Pre-Trained Language Models

Latest innovation in language models have enhanced the Natural Language Processing (NLP) capacity. Based on novel Transformer architectures BERT released by Google as well as other BERT-like models made truly a breakthrough in NLP. This enabled hundreds of practical applications and use cases to be productized by many companies.

Business Implication:

  • Text classification and categorization
  • Chatbots and question answering (QnA) system
  • Other: sentiment detection, machine translation, named entity recognition, summarization

Applied Reinforcement Learning

Reinforcement learning has a wide use in various industries where the decisions or outcomes are completely autonomous. Earlier RL models are confined to research space, whereas now companies are evaluating on using RL models to simulate real time environment where they can test their predictive models or train bots.

Business Implication:

  • Simulate real time scenarios for their business process and test algorithms
  • Defining trading strategies and transaction simulation
  • RL Based simulation process to test customer behaviour
  • Utilization in gaming industry for designing human like AI bots

Model Interoperability

As there are wide range of deep learning frameworks, there is a need to have a framework which can help in transferring the model from one Deep learning framework to other. For Example, there may be need to convert a model which is built using TensorFlow into PyTorch without any parameter loss.

Business Implication:

  • Capability to interoperate models based on different Deep Learning Frameworks
  • Deploy models using various frameworks and platforms

Deep Fakes and Deep Fake Detectors

Advancement in Deep Learning technology have paved the way for synthesising human face reactions artificially. Deep Fakes technology has the potential to revolutionize video content creation space where designer can use them to generate new videos without requiring the real person. There are various videos released where the faces of the artists in the video are replaced with some other person.

Business Implication:

  • Content generation- for generating new video content without requirement of artists physical presence after getting due permissions

How should I follow all these trends?

These are just few trends that start to be utilized by some of business players. To stay competitive on the market and leverage full power of AI its right time to think on your data science team. In-house or outsource?

If you need any support in implementing state-of-the-art AI technologies – just send contact us. We will get in touch with you immediately.

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