Overcoming the Barriers of the Physical World with AI

The rapid advancement of artificial intelligence (AI) is revolutionising our lives and work, making processes more efficient. Technologies like large-scale machine learning and natural language processing models, such as ChatGPT, are pushing the boundaries of what was once confined to the realm of science fiction. However, a significant challenge remains in bridging the gap between technical brilliance and real-world application.

While AI has made significant progress in virtual environments, the introduction of AI-powered general-purpose robots in the physical world still faces substantial obstacles. Why is this the case, and how can we address these barriers? We explore the topic in more detail below.

Energy efficiency stands out as a primary obstacle. At its core, a robot is essentially a self-propelled computer. Anyone who has used a laptop knows that even the best devices struggle to operate for more than a few hours without recharging. With robots, energy demands are even higher due to internal processes and physical movement. Safety considerations prevent them from relying on tethered connections, necessitating extended battery life.

Unfortunately, current robot mechanics and autonomous systems lack the energy efficiency required for sustained operation. They require frequent and extended charging periods to perform optimally. While the first generation of robots is utilised in industrial settings for manufacturing, they remain constantly tethered to a power source. Although there are general-purpose robots available, like Sanctuary’s Phoenix humanoid, they are still cumbersome and expensive. It will likely take five to ten more iterations before we achieve a model that is truly independent, freely moving, and capable of performing various tasks.

To bridge this gap, we must start with smaller and simpler applications that gradually lead to full AI integration in the physical world. Cobots, which are robots designed for simple tasks, can play a crucial role in this process. Examples include self-driving wheelchairs, robots cleaning building facades, or autonomous technology performing complex, focused tasks like a smoke-diving robot searching for people or a drone fixing power lines. The key is focusing on single-duty performance, not only to enhance energy efficiency but also to achieve the highest standard of work.

Mechanical efficiency is another critical aspect. By improving the way robots move, potentially by utilising artificial muscles and joints to mimic human motion, we can reduce their energy requirements. However, achieving fully functional humanoid technology is still a considerable distance away.

To Know More, Read Full Article @ https://ai-techpark.com/overcoming-barriers-with-ai/ 

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Embracing Quantum Machine Learning to Break Through Computational Barriers

In our previous articles, we have highlighted how machine learning (ML) and artificial intelligence (AI) can revolutionize IT organizations. But there is another very powerful resource that has the potential to change the traditional way of computing, which is called quantum computing (QC). In today’s article, we will highlight how to overcome computing limitations with quantum machine learning (QML) and what tools and techniques this technology can offer. But first, let’s take a quick glimpse of what quantum computing is.

Quantum computing is currently an emerging field that requires the development of computers based on the principles of quantum mechanics. Recently, scientists, technologists, and software engineers have found advancements in QC, which include increasingly stable qubits, successful demonstrations of quantum supremacy, and efficient error correction techniques. By leveraging entangled qubits, quantum computing enables dramatic advances in ML models that are faster and more accurate than before.

Usefulness of Utilizing Quantum Computing in Machine Learning

Quantum computing has the power to revolutionize ML by allowing natural language processing (NLP), predictive analytics, and deep learning tasks to be completed properly and with greater accuracy than the traditional style of computing methods. Here is how using QC will benefit technologists and software engineers when applied properly in their company:

Automating Cybersecurity Solutions

As cybersecurity is constantly evolving, companies are seeking ways to automate their security solutions. One of the most promising approaches is QML, as it is a type of AI that uses quantum computing to identify patterns and anomalies in large-scale datasets. This allows the companies to identify and respond to threats faster and reduce the cost of manual processes.

Accelerate Big Data Analysis

Quantum computing has gained traction in recent years as a potentially revolutionary technology that can be accurate in computing tasks and improve the speed of completing tasks. However, researchers are nowadays investigating the potential of QML for big data analysis. For example, a team of researchers from the University of California recently developed a QML algorithm that can analyze large-scale datasets more quickly and accurately than traditional ML algorithms.

The potential of QML algorithms is immense, and training them properly can be a major challenge for IT professionals and technologists. Researchers are finding new ways to address these problems related to the training of quantum machine learning algorithms.

To Know More, Read Full Article @ https://ai-techpark.com/overcoming-limitations-with-quantum-ml/ 

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