Large language models (LLMs) solve natural language processing problems with astounding accuracy. However, these models are enormous and require a lot of space, cost, and computation power to deploy. For example, the GPT-175B model has 175 billion parameters requiring 320GB of storage and at least 5 A100 GPUs with 80GB of memory each for inference. This computation power is expensive, making this solution only viable for some organizations. Hence, deployment of such models falls outside the purview of small organizations and individuals.

Deploying a huge model with millions of parameters is not easy, what if we could transfer the knowledge to a smaller model and use it for inference? At the end of this article you will understand how it can be done. Knowledge Distillation means transferring the knowledge of a bigger model to a smaller one with the minimum loss of information. It can also refer to transferring the knowledge of multiple models (ensemble) into a single one. In most of the cases, we use the same model for training and inference.

This paper proposes a neural architecture search (NAS) method for split computing. Split computing is an emerging machine-learning inference technique that addresses the privacy and latency challenges of deploying deep learning in IoT systems. In split computing, neural network models are separated and cooperatively processed using edge servers and IoT devices via networks. Thus, the architecture of the neural network model significantly impacts the communication payload size, model accuracy, and computational load. In this paper, we address the challenge of optimizing neural network architecture for split computing. To this end, we proposed NASC, which jointly explores optimal model architecture and a split point to achieve higher accuracy while meeting latency requirements (i.e., smaller total latency of computation and communication than a certain threshold). NASC employs a one-shot NAS that does not require repeating model training for a computationally efficient architecture search. Our performance evaluation using hardware (HW)-NAS-Bench of benchmark data demonstrates that the proposed NASC can improve the ``communication latency and model accuracy" trade-off, i.e., reduce the latency by approximately 40-60% from the baseline, with slight accuracy degradation.

Let's analyze our own bodies. We the humans begin to have six senses like touch, smell, vision, hearing, tasting, and the sixth sense. I'm not sure some researchers are saying the sixth sense is about sensing one's body in space. Our human body is fully connected with nerves whenever you touch or taste anything you will feel or sense something about that action you have done. How you are able to classify the sense whether that it is good or bad?

Most technologies start with big hype in the industry and step-by-step lose their attention since they can not meet our expectations in solving real-world problems. Artificial Intelligence is one of the only recent technologies that have met and beaten its expectations. However, if we do not have a solid strategy in pursuing AI development, this technology may end up at the same destination as others. The AI strategy has three main pillars: Feasibility, Performance, and Scalability. Nowadays, everyone talks about the potentials of AI models but I want to pinpoint issues that must be investigated carefully in order to create a perfect AI strategy.

The majority of significant breakthroughs in computer science and Artificial Intelligence have been the result of an explosive increase in computation power. Up to this point, growth has been exponential, with computational power roughly doubling every 2 years. At the same time the complexity of deep learning tasks is increasing even more rapidly. So there is a widening gap between the computation power needed for modern AI applications and the computational resources we have access to, and it is very quickly becoming a major issue. Many times in the past humanity has turned to Mother Nature in order to find solutions to complex engineering problems.

Many companies including: OpenAI, Google/DeepMind, Microsoft, and countless others, have started the race for "truly" intelligent AI. For the majority of this article I'll be referencing OpenAI's GPT series of machine learning models. However, the question: "what does it mean to be truly intelligent?" OpenAI have modeled this problem as a text transformer model. This model takes sequences of pieces of words (two character pairs) and tries to predict the next set of word parts.

Machine learning is method in which we make the machines intelligent. It is a study of computer algorithms. Machine learning algorithms are mathematical models which predicts or make desicions based on sample data, without being explicitly programmed to do so. The sample data on which the model make prediction.....

Artificial intelligence (AI) has traditionally been deployed in the cloud, because AI algorithms crunch massive amounts of data and consume massive computing resources. But AI doesn't only live in the cloud. In many situations, AI-based data crunching and decisions need to be made locally, on devices that are close to the edge of the network. AI at the edge allows mission-critical and time-sensitive decisions to be made faster, more reliably and with greater security. The rush to push AI to the edge is being fueled by the rapid growth of smart devices at the edge of the network--smartphones, smart watches and sensors placed on machines and infrastructure.

Artificial intelligence (AI) has traditionally been deployed in the cloud, because AI algorithms crunch massive amounts of data and consume massive computing resources. But AI doesn't only live in the cloud. In many situations, AI-based data crunching and decisions need to be made locally, on devices that are close to the edge of the network. AI at the edge allows mission-critical and time-sensitive decisions to be made faster, more reliably and with greater security. The rush to push AI to the edge is being fueled by the rapid growth of smart devices at the edge of the network--smartphones, smart watches and sensors placed on machines and infrastructure.