To fill these gaps, non-AI technologies can be useful.
Artificial intelligence (AI) is an emerging computer technology that incorporates artificial intelligence. It is widely believed that the AI applications we see in our daily lives represent only the tip of the iceberg of its power and capabilities. The field of AI needs continuous evolution and development to overcome common AI limitations.
Artificial intelligence typically comprises the following subfields:
Machine Learning
Machine learning combines the use of data from neural networks, general and specific statistics, operational discoveries, and other sources to discover patterns in information without external guidance. Deep learning uses neural networks containing multiple layers of complex processing units. Deep learning uses larger datasets to provide complex outputs, such as speech and image recognition.
Neural Networks
Neural networks, also known as artificial neural networks, use digital and mathematical information for data processing. A neural network contains multiple data nodes similar to neurons and synapses, simulating the function of the human brain.
Computer Vision
Computer vision uses pattern recognition and deep learning to identify content in images and videos. By processing, analyzing, and acquiring knowledge about images and videos, computer vision helps artificial intelligence interpret its surroundings in real time.
Natural Language Processing
These are deep learning algorithms that enable artificial intelligence systems to understand, process, and generate human spoken and written language.
Non-AI technologies that make AI more advanced (or at least reduce its limitations) typically enhance one of the components or positively influence its input, processing, or output capabilities.
Semiconductors: Improving Data Movement in Artificial Intelligence Systems
The coexistence of semiconductors and artificial intelligence systems in the same space is quite common. Numerous companies manufacture semiconductors for AI-based applications. Established semiconductor companies are implementing dedicated programs to manufacture AI chips or embed AI technology into their product lines. A prominent example of these organizations' involvement in AI is NVIDIA, whose graphics processing units (GPUs), which contain semiconductor chips, are widely used in data servers for AI training.
Modifications to semiconductor structure can improve the data utilization efficiency of AI-driven circuits. Changes in semiconductor design can increase the data movement speed of AI memory storage systems. In addition to increasing power, storage systems can also become more efficient. With the involvement of semiconductor chips, there are several ideas to improve various aspects of data utilization in AI-driven systems. One idea is to send data to or from a neural network only when needed (rather than constantly sending signals through the network). Another advancing concept is the use of non-volatile memory in AI-related semiconductor designs. Non-volatile memory chips are known to retain saved data even when power is off. Integrating non-volatile memory with processing logic chips can create dedicated processors to meet the growing demands of newer AI algorithms.
While the demands of AI applications can be met through improvements in semiconductor design, this can also present certain manufacturing challenges. AI chips are typically larger than standard chips due to their high memory requirements. Therefore, semiconductor companies will need to invest significantly more in manufacturing them. Consequently, manufacturing AI chips is not economically viable for businesses. To address this issue, general-purpose AI platforms can be used. Chip suppliers can enhance these types of AI platforms with input/output sensors and accelerometers. Leveraging these resources, manufacturers can tailor the platform to evolving application requirements. The flexibility of general-purpose AI systems can be cost-effective for semiconductor companies and significantly reduce the limitations of AI.
Internet of Things (IoT): Enhancing AI Input Data
The introduction of artificial intelligence (AI) into the Internet of Things (IoT) both enhances its functionality and seamlessly addresses its respective shortcomings. The IoT, as we know it, encompasses a variety of sensors, software, and connectivity technologies, enabling multiple devices to communicate and exchange data with each other and with other digital entities via the internet. These devices range from everyday household items to complex organizational machines. Essentially, the IoT reduces the human element in multiple interconnected devices that can observe, determine, and understand situations or their surroundings. Devices such as cameras, sensors, and sound detectors can record data autonomously. This is where AI comes in. Machine learning always requires its input datasets to be as broad as possible. The IoT, with its vast array of interconnected devices, provides an even wider dataset for AI research.
To fully leverage the massive amounts of data from the Internet of Things (IoT) for AI-driven systems, organizations can build custom machine learning models. By utilizing IoT's ability to collect data from multiple devices and present it in an organized format on a sleek user interface, data experts can effectively integrate it with the machine learning components of AI systems. The combination of AI and IoT is effective for both systems because AI can process vast amounts of raw data from its IoT counterparts. In return, AI can quickly identify patterns in information to organize and provide valuable insights from large amounts of unclassified data. AI's ability to intuitively detect patterns and anomalies from a set of disparate information is complemented by IoT sensors and devices. By generating and simplifying information through IoT, AI can handle a great deal of detail related to different concepts such as temperature, pressure, humidity, and air quality.
In recent years, many large enterprises have successfully deployed their own interpretations of the combination of artificial intelligence and the Internet of Things to gain a competitive advantage in their respective fields and address the limitations of artificial intelligence. Google Cloud IoT, Azure IoT, and AWS IoT are some prominent examples of this trend.
Graphics Processing Unit: Provides computing power for AI systems
With the increasing prevalence of artificial intelligence, GPUs have transformed from simple graphics-related system components into an indispensable part of deep learning and computer vision processes. In fact, GPUs are widely considered to be AI versions of CPUs in ordinary computers. First and foremost, systems require processor cores to perform computational operations. Compared to standard CPUs, GPUs typically contain a greater number of cores. This allows these systems to provide better computing power and speed for multiple users in multiple parallel processes. Furthermore, deep learning operations process massive amounts of data. The processing power and high bandwidth of GPUs can effortlessly meet these requirements.
Because of their powerful computing capabilities, GPUs can be configured to train artificial intelligence and deep learning models (often simultaneously). As mentioned earlier, the greater bandwidth gives GPUs a necessary computational advantage over conventional CPUs. Therefore, AI systems can tolerate input of large datasets, potentially overwhelming standard CPUs and other processors, resulting in significantly larger outputs. Crucially, GPU usage in AI-driven systems does not consume vast amounts of memory. Typically, computing large, diverse jobs involves multiple clock cycles in a standard CPU, as its processors complete jobs sequentially and have a limited number of cores. On the other hand, even the most basic GPU has its own dedicated VRAM (Video Random Access Memory). Therefore, the main processor's memory is not burdened by small to medium-sized processes. Deep learning requires large datasets. While technologies like the Internet of Things (IoT) can provide a wider range of information, and semiconductor chips can regulate data usage in AI systems, GPUs offer both computational power and greater memory reserves. Therefore, GPU usage limits the processing speed limitations of AI.
Quantum computing: A comprehensive upgrade to artificial intelligence
On the surface, quantum computing appears similar to traditional computing systems. The key difference lies in the use of unique qubits, which allow information within a quantum computing processor to exist simultaneously in multiple formats. Quantum computing circuits perform tasks similar to conventional logic circuits, but with the addition of quantum phenomena such as entanglement and interference, elevating their computation and processing to the level of supercomputers.
Quantum computing allows artificial intelligence systems to extract information from specialized quantum datasets. To achieve this, quantum computing systems use multidimensional arrays of numbers called quantum tensors. These tensors are then used to create massive datasets for AI to process. Quantum neural network models are deployed to discover patterns and anomalies within these datasets. Most importantly, quantum computing improves the quality and accuracy of AI algorithms. Quantum computing eliminates common limitations of artificial intelligence in the following ways:
● Compared to standard systems, quantum computing systems are more powerful and less prone to errors.
● Generally speaking, quantum computing is helpful for open-source data modeling and machine training frameworks for artificial intelligence systems.
● Quantum algorithms can improve the efficiency of artificial intelligence systems in finding patterns in entangled input data.
We can clearly see that the development of artificial intelligence can be achieved by increasing the amount of input information (through the Internet of Things), improving data utilization (through semiconductors), increasing computing power (through GPUs), or improving various aspects of its operation (through quantum computing). Beyond these, other technologies and concepts may also become part of the future development of artificial intelligence. More than sixty years have passed since the concept and birth of artificial intelligence, and today it is more important than ever in almost every field. Wherever the next stage of AI evolution leads, it will be fascinating.
