Challenge everything you’ve tried so far: the next-generation AI algorithms bring brand-new, awesome ways to solve problems, innovate, and bring out the very best in humans

The age of guessing is over. The era of algorithms is here. Much of the current love for AI comes from deep learning on neural networks. These are brute force, pattern recognition machines that – if provided with plenty of training data – can go where the more traditional data science cannot. Deep learning can be combined with other technology-enabled tactics, such as reinforcement learning, to provide even more unparalleled problem-solving power. Building these algorithms is a specialized, energy-consuming task, but off-the-shelf algorithms and models provide sensible, sustainable alternatives.

Padmashree Shagrithaya Expert in Residence


  • Many current advances in AI are thanks to machine learning models on neural networks, detecting and classifying features through multiple layers of raw input.
  • With abundant training data as an input, neural networks may recognize patterns much more effectively than traditional (statistical) data science approaches.
  • Advances in the ability to collect, store, and access training data, plus powerful graphical processing units (such as GPUs), have been instrumental to its success.
  • Reinforcement learning uses an action/reward approach to learn from interactions. This creates additional AI power in areas such as robotics, scheduling, and gaming.
  • Training AI models consumes a lot of energy, but the resulting models can optimize energy consumption of a variety of business activities, providing a net gain scenario.
  • Training AI models also requires highly specialized skills, but low-code AutoML (Automated Machine Learning) tools bring AI algorithms to a much wider audience.
  • Approaches such as TinyML offload trained AI models to even the smallest of edge devices, relieving energy-consuming central facilities.


  • Microsoft developed an image-captioning algorithm that exceeds human accuracy in identifying objects, but also more precisely describes the relationship between them. It is incorporated within its assistant app for the visually impaired, “Seeing AI.”
  • A life science research team developed a reinforcement learning-based AI pancreas that calculates the amount of insulin needed for a diabetic patient and injects it automatically. It is hailed as “autonomous driving for the medical industry.”
  • Tokio Marine uses deep learning computer vision to auto process damage insurance, analyzing photos of damaged vehicles and providing recommendations on repair options, paint and blend operations, and the expected number of labor hours.


  • Solving problems that were deemed impossible to solve – or insufficiently successful – with more classic data science approaches.
  • Creating powerful, self-learning, and self-optimizing autonomous systems decreases the need for scarce onsite human resources.
  • Extracting more value out of (historical) data by turning them into powerful AI algorithms that can be monetized externally.
  • Acquiring access to next-generation AI algorithms without the need for scarce, specialized resources – through AutoML, low-code AI, and off-the-shelf models.
  • Lowering the energy consumption of large, central AI computing environments through downloading trained “inference” models to edge devices.
  • Using superior AI algorithms for optimizing scare natural and human resources, to battle climate change, and in general contribute to better societal futures.

Find Out More

Visit our website to delve into Technovision trends and resources:

TechnoVision Report

To access and download the TechnoVision 2022 report as a PDF, click here:

Contact Us

If you would like to know more about TechnoVision from Capgemini, or to speak to us about any aspects of this report, click here: