In a nutshell, AI technology enables computers to think in a similar way to humans. This means that when faced with a task, an AI system will take in information from its environment, select an appropriate response, and learn from any mistakes it makes to ensure success the next time around. AI is actually an umbrella term for a wide range of technologies, including machine learning and natural language processing. Today, it’s found in everything from smartphones and appliances to autonomous vehicles and even healthcare. And that’s just the beginning; it won’t be long before AI technology is as ubiquitous as it is convenient.
Perhaps the simplest way to think of AI is as technology that enables devices to perform tasks that require human-like cognition. Image and speech recognition are clear markers of such intelligence, and two areas where AI is rapidly advancing. Image recognition systems are quickly becoming capable of not just recognizing objects and facial expressions, but also the context and nuance behind them. Some can even utilize this insight to generate completely new, ultra-realistic images. Speech recognition systems are leveraging deep learning to analyze billions of words, along with complex phrases and sentence structures, and enable intelligent assistant services to understand and respond to users’ sophisticated commands.
The process of enabling devices to think for themselves begins with an AI concept known as machine learning.
The idea is simple: rather than programming a computer to perform a certain task, you teach it to identify patterns in data and make reliable decisions on its own. Deep learning is a subset of machine learning in which artificial neural networks – algorithms inspired by the structure of the human brain – are used to identify patterns in large amounts of data. Deep learning can be applied to support complicated functions like automatic language translation, and it’s ideal for
large-scale social and business applications.
TIt’s one thing to train a system to recognize what it’s seeing. It’s another to ask that system to draw from data it’s analyzed and ‘imagine’ a completely new outcome – or a concept or object it’s never seen before. Training AI systems to make inferences has proven difficult thus far, but thanks to advancements in deep learning, we’re getting closer all the time. Once AI systems become capable of reasoning, they’ll be able to ‘think’ more flexibly, just like humans, and make inferences about the relative relationships between objects they recognize. This sort of relational intelligence is poised to revolutionize a variety of fields, including the automotive and manufacturing industries, as well as finance and security.
Typically, when a person makes a decision, they consider not just its immediate impact, but any future ramifications as well. Sometimes, looking at the choice from a long-term perspective leads us to choose an option that may not be the best for the moment, but will pay off in the long run.
Many AI systems today are trained to do the same. Taking a big-picture approach to decision-making, they ‘imagine’ the outcome of each option before ultimately selecting the choice that’s most likely to achieve the end goal – even if that choice presents difficulties in the short term.
Machine learning is the process which enables AI to analyze complex data and anticipate future actions automatically. By categorizing data with labels through supervised learning and identifying patterns in data sets via unsupervised learning, the process gives machines the ability to help us make decisions quicker and with greater accuracy. With reinforcement learning methods, a process which resembles how people and animals learn through trial and error, machines and devices can expand their capabilities independently without explicit programming. Together, these processes form the foundation for all AI-enabled features and functionalities.
Thanks to deep learning, the devices can now analyze and recognize input data such as images and objects with incredible accuracy. This capability is enabled by artificial neural networks (ANNs) composed of interconnected layers of algorithms, known as neurons, that are capable of processing and learning from data in a similar way to us. Deep Neural Network (DNN) is an artificial neural network that contains multiple layers between the input and output. Similar to the way a human brain functions, DNN operates by passing the input through the layers of connected neurons for processing. Convolution, a linear mathematical operation, is typically employed to identify patterns in data for image, speech, and natural language processing.
One of the most exciting applications for AI involves the processing of Big Data – or data sets so large and complex that they cannot be processed using traditional techniques. Businesses prioritize Big Data because, if analyzed properly, such data sets might reveal valuable insights that could aid them in decision making. With AI, analysts will be able to feed massive amounts of data into a machine-learning algorithm that’s capable of sifting through and analyzing the information much faster and more efficiently than a human ever could – making it easier for enterprises to capitalize on any insights that the data may hold.
Taking AI to the next level will require advancements in high-performance computing (HPC). HPC, which describes the ability to process data and carry out complex calculations at speeds that most computers and servers simply cannot match, is currently being used to manage vast amounts of data for a variety of uses, including high-performance data analytics and the training of machine learning models. By enabling parallel processing, in which compute servers – known as nodes – work together to boost processing power, HPC allows systems to run advanced, large-scale applications quickly and reliably. Such efficiency adds up to dramatic increases in throughput, which is necessary for processing the exponential amounts of data that come with AI.
Advancements in on-device AI will play a key part in making connected devices faster and more efficient. Rapid improvements in AI algorithms, hardware and software are making it possible to shift AI services away from the cloud and onto our devices themselves. Localizing these services on mobile devices, appliances, cars and more presents exciting benefits in terms of reliability, privacy and performance. Not only does on-device AI resolve issues related to network connectivity, it’s also much faster than the cloud because it doesn’t require data to be transmitted to and from a server, and it enables biometric and other sensitive data to be safely confined to the user’s device.
Advancing AI
on mobile devices
Taking HPC
to the next level
Advancing AI
in automobiles
Advancing AI
in entertainment
In addition to enabling much faster processing, greater reliability and tighter data security, on-device AI will revolutionize how we utilize our mobile devices.
AI-powered cameras, for example, are already optimizing photos with better image processing, and enhancing biometric security by providing more accurate facial recognition. Virtual and augmented reality experiences, too, will become more immersive and interactive when AI processing is localized to mobile devices. It will also make virtual assistants smarter and more useful by moving vital functions like natural language processing and speech recognition away from the cloud.
The impending influx of AI services and technologies will unlock new and dynamic applications for high-performance computing (HPC). Applications like live-streaming services, which require massive amounts of data to be processed in real time, will deliver crisp and clear content thanks to lightning-fast, HPC-powered IT infrastructures. HPC clusters will also benefit from increased efficiency, facilitating speedy data transmission between compute servers and storage. In addition, the costs associated with supporting HPC will lower as cluster architectures become more efficient at managing resources – lowering businesses’ TCO (total cost of ownership).
Not only has AI paved the way for the development of self-driving cars, it also holds the keys to making our commutes safer and more efficient. Connected vehicles employ dozens or even hundreds of sensors to, among other functions, 1) detect potential hazards before drivers see them, and take control of the wheel to avoid accidents, 2) monitor critical components to help prevent failure, and 3) monitor the driver’s gaze and head position to detect when they may be distracted or drowsy. Talk about driving innovation!.
Artificial intelligence is changing the way we enjoy our favorite entertainment by enabling smart TVs to truly live up to their name.
Manufacturers like Samsung are using AI to offer users more personalized content recommendations, and allow them to control their TVs with simple voice commands. In addition, several of Samsung’s latest TVs utilize machine learning to enable users to enjoy their favorite content in the most immersive resolution available: 8K. A built-in AI processor upscales content of all kinds into crystal clear 8K, taking users’ viewing experiences to the next level.
Driving AI innovation with a comprehensive portfolio of AI solutions, Samsung is paving the way for the implementation of smarter, more personalized and ubiquitous AI.
Samsung’s Exynos mobile processors feature advanced NPUs (neural processing units) for more powerful and efficient on-device AI, while its memory solutions, including the LPDDR5 mobile DRAM, are optimized to handle AI systems’ demanding performance requirements. The company’s HBM2E (high bandwidth memory) solutions offer the performance, capacity and efficiency required to support next-generation AI technologies, while its low-latency storage devices, including the Z-SSD, are engineered to manage AI and HPC |workloads with ease. Memory solutions like the Samsung AutoSSD are advancing smart car development by enabling effective, high-speed vehicle-driver feedback, while Exynos Auto processors offer AI capabilities through integrated NPUs.
Trusted reliability
Designed to be used in high-performance servers, desktops, laptops and more, Samsung's DDR (Double Data Rate) solutions double down on performance, combining high bandwidth with likewise high energy efficiency.
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Next-level performance
Samsung's HBM (High Bandwidth Memory) solutions have been optimized for high-performance computing (HPC), and offer the performance needed to power next-generation technologies, such as artificial intelligence (AI), that will transform how we live, work and connect.
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Life in the fast lane
Samsung's GDDR (Graphics Double Data Rate) solutions are optimized to process vast amounts of data, delivering lightning-fast speeds for server applications such as fast-tracking graphics processing. Whether on servers, PCs, or workstations, GDDR can be harnessed for video processing, gaming and more.
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A powerful combination
Samsung's LPDDR (Low-Power Double Data Rate) solutions support speedy, high-bandwidth data transfers without compromising energy efficiency.
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Optimized fordata center storage
Samsung's data center SSD allows data center architects to achieve reliable, high storage capacity that delivers on performance.
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Redefining fast responsiveness
Stay ahead of the game in today's data-centric climate with Samsung Z-SSD
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Powering the future of mobile storage
This is flash storage engineered to keep up with the growing demands of 5G.
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Designed for mobile innovation, Exynos enables richer user experiences with intelligent performance, greater efficiency and seamless connectivity.
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* All product specifications reflect internal test results and are subject to variations by user’s system configuration.
* All product images shown are for illustration purposes only and may not be an exact representation of the product.
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