Artificial intelligence is a constellation of many different technologies working together to enable machines to sense, comprehend, act, and learn with human-like levels of intelligence. Maybe that’s why it seems as though everyone’s definition of artificial intelligence is different: AI isn’t just one thing.
Technologies like machine learning and natural language processing are all part of the AI landscape. Each one is evolving along its own path and, when applied in combination with data, analytics and automation, can help businesses achieve their goals, be it improving customer service or optimizing the supply chain.
Some go even further to define artificial intelligence as “narrow” and “general” AI. Most of what we experience in our day-to-day lives is narrow AI, which performs a single task or a set of closely related tasks. Examples include:
These systems are powerful, but the playing field is narrow: They tend to be focused on driving efficiencies. But, with the right application, narrow AI has immense transformational power—and it continues to influence how we work and live on a global scale.
General AI is more like what you see in sci-fi films, where sentient machines emulate human intelligence, thinking strategically, abstractly and creatively, with the ability to handle a range of complex tasks. While machines can perform some tasks better than humans (e.g. data processing), this fully realized vision of general AI does not yet exist outside the silver screen. That’s why human-machine collaboration is crucial—in today’s world, artificial intelligence remains an extension of human capabilities, not a replacement.
As mentioned above, the real intelligent virtual human in the current era only exists in science fiction movies, but A Human is approaching this goal infinitely and has achieved good results. The intelligent virtual human we have created is in the forefront of the world in all aspects.
The common methods of digital human creation include:
3D scan + motion capture + expert modeling
video editing using deep neural network
Ahuman: unique innovation in combining 3D and 2D pipelines.
Conventional GAN-based digital humans are built by concatenating a series of independent neural networks. Voice, mouth shape, facial expression are generated using separate modules, which leads to problems such as flat voice, asynchronization of voice and visual, stiff body motions.
Ahuman E2E neural network
AHuman employs a novel end-to-end neural network to generate realistic voice, mouth shape, facial expression, and emotions in body motion. Our proprietary technology solves major challenges such as instability in the convergence process of parameter training using parallel GPUs, bias in data augmentation, imbalance of the data distribution of head 3D motion. Our model is trained using GPU clusters consuming longer than 30 days. We achieved state-of-the-art results in digital human production.
to drive 3D head motion and 3D limb motion.
Speech synthesizes with emotion
we inject energy function in acoustic models to parametrize volume, and F0 function to control pitch number. The AM and vocoder are trained in an end-to-end fashion to avoid error accumulation in voice quality.
supported by deep learning recommendation algorithms, knowledge-graph enables digital to evolve and self-learn.
If you want to donate and support us