AI Training and capabilities

AI Training and Capabilities

Creating Artificial Intelligence that can learn by itself

Throughout history, we have used tools to save time and energy and enable ourselves to achieve things on a scale that was impossible before.

Today, AI is poised to help us manage data, automate processes and create media on an unprecedented scale. Still, it remains challenging to use and unaffordable for the vast majority of users. offers a user-friendly Studio where anyone can access AIs and use them for an unlimited number of applications, with synthetic media as just the first of many more to come. The community-based approach coupled with blockchain infrastructure will allow users to access the AI products at affordable prices.

Currently, we are at a tipping point with regard to the power of AI capabilities. These technological innovations will soon change how we interact with communication, entertainment, education, healthcare, banking, insurance, and other industries.

For example, AI models can be trained and deployed to detect fraudulent
financial activity or optimize sales and marketing based on consumer behaviour. Financial services (along with other industries) can integrate trained virtual sales and customer support agents. Online retailers can benefit from AI algorithms to analyze user comments and reviews and customize the online experience for the shoppers, making it even more engaging when transforming plain text pages into short ads showcasing their wishlist products. Marketers can use AI tools to analyze and manipulate voice when interacting with customers, assess user behaviour and sentiment to generate more relevant offers, or personalize the experience of using a product or service. The opportunities are limitless.

Creating AI that can learn by itself

While strong progress has been achieved in artificial intelligence-related tasks, e.g. image classification, object/behavior recognition etc., the standard machine learning approaches were designed to be trained in a static manner. In contrast, real-world data are often dynamic and the algorithms should be able to cope with the new information without requiring a full retraining. In other words, it is not enough for AI systems to learn, but it is important that they learn continuously. This is similar to what humans (and biological systems in general) do, i.e., are able to accumulate knowledge without forgetting what has been learn previously (called catastrophic forgetting by specialized literature).

There is a large community in machine learning that is dealing with what is called Continual Learning or Lifelong Learning. These approaches are interesting as they avoids training from scratch when new data are available, thus reducing the computational requirements and memory footprint. This memory limitation could be of critical importance in robotics, edge-based systems (such as mobile devices), and when the regulations limit the data storage and distribution (as is the case of health-related data).

In most of these systems there is a data life cycle. The cycle starts by feeding the AI system with new data which are used to update the knowledge of the system in a supervised manner. After learning the new data, the system is evaluated on a separate test set before is being fed again with new data. Note that while the AI system receives the tasks sequentially, it can access data from a single task while being evaluated on all tasks seen so far. In some cases, a bounded memory of past data is allowed.

What can we expect in the future

The performance of incremental learning methods is getting closer to that of standard learning, where all data from all classes are available at all times. However, the catastrophic forgetting problem is far from being solved, especially at a large scale. Most of the existing systems use a memory of the past but this many not be always available in real-life scenarios. Additionally, memory-dependent algorithms tend not to scale well when more and more classes will have scarce representative data when the maximum size of the memory is reached. Class-incremental learning without memory is understudied in the literature and more effort should be allocated to it. Also, the generalization power of our algorithms (e.g., performing domain adaptation, using zero-shot learning, and novel class discovery) should be improved.

Creating artificial content, one of the main activities of, is a way forward to address the scarcity of available data as it can provide a good solution for data augmentation that considers not only the diversity of the data but can also incorporate the human preferences. Overall, the expectation is that our algorithms and systems are able to learn in a similar way to humans and this will bridge the gap between biological and artificial intelligence.

About is a Web 3.0 company that brings together an ecosystem of stakeholders around the use of AI to create at scale. Introducing the first framework for ethical AI and blockchain, is creating an all-in-one platform for AI-based creation and governance. Through its creative studio, token-based ownership and accountability system, is designed to ensure contributions are fairly rewarded and that every AI is kept honest over the long term.

The native token of the ecosystem, the $HEART token, empowers users to participate in the governance of the platform and facilitates key flows of value within it. All fees charged within the platform will be paid in $HEART.

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