What’s next for AI?

AI technology is at the forefront of today’s groundbreaking developments - from the detection of brain bleeds, prediction of diseases in wheat crops, and to optimization of hospital surgery rooms, and automation of giant mining equipment. The reason for the increasing adoption of AI is its ability to create significant  business value for organizations and enterprises.

The heart of the AI revolution in the last decade is Deep Learning. For the first time, humankind has the ability to mimic the operation of the human brain in computers, enabling us to solve previously unsolvable problems.

The neuron, the most basic computational unit that makes up our brains, is simulated in computers, assembling Artificial Neural Networks. These are the core of Deep Learning.

However, the gap between AI and human capabilities is still substantial as AI has a significant drawback: it requires huge amounts of labeled data. Frequently, it is impossible to obtain such data (it is impossible to obtain more than thousands of patients in clinical trials, it is impossible to obtain a history of thousands of malfunctions in heavy machines as fortunately they simply did not occur). Unlike AI, which needs millions of images of faces to be able to differentiate between people, a baby recognizes its mother in its first moments in the world.

These are gaps that limit the impact of AI technology, especially in the areas in which we focus - operational excellence, sustainability and safety.

Few-Shot Learning is an active field of research that addresses this gap. By using these tools, the amount of labeled data that is required for Neural Network training decreases dramatically, to the point where only few examples are sufficient to train the model.

Imagine you lift a cup and rotate it in front of you. In terms of your visual system, this is unlabeled data. Afterwards, a single understanding that this object is called a ‘cup’ is enough for you to detect any further cup. Artificial neural networks can be trained in a similar fashion.

The Generative Pre-trained Transformer 3 (GPT-3) model recently built by OpenAI is an amazing demonstration of almost human-level Few-Shot Learning capabilities. This is the largest AI model ever built, with approximately 175 billion parameters, roughly 0.1% of the amount of connections in the human brain (this is a rough comparison as biological neurons are more complex than artificial ones).

The model studied huge unlabeled text repositories from across the web, and succeeds by one or a few individual examples:

Answer free questions

Develop mobile applications

And even write stories

In a similar fashion, if we want to predict faults in a new industrial machine with only a few faults in history, or optimize a brand new surgery room, or detect a safety hazard in a new environment, we will not start the model training from scratch, but from our proprietary data library, creating a vast preliminary knowledge. Thus, only a few new examples would suffice to achieve state-of-the-art results, getting closer to human-level intelligence.