it is a presentation based on research paper written by scientists on machine learning revolution in 2018.its major concepts and initial info is given.
Empowering the machine learning evolutionErumShammim
This document summarizes machine learning technologies and their growth. It discusses how big data and increased computing power like GPUs have enabled advances in machine learning. It describes Google's tensor processing unit (TPU) which is an AI chip designed for neural networks. The TPU has increased performance and bandwidth in its second generation. The document also briefly mentions applications and pitfalls of machine learning.
This document discusses key technologies of the future as identified by the Technology Information, Forecasting & Assessment Council (TIFAC). It outlines several technologies including 3D printing, advanced oil and gas exploration, advanced robotics, alternate fuels, artificial intelligence, autonomous vehicles, big data analytics, brain-computer interfaces, cloud technology, digital holography and 3D imaging, energy storage technologies, gamification, immersive virtual reality, internet of things, lab-on-a-chip, quantum computing, real-time translation, semantic web, telemedicine, and wearable devices. TIFAC sees these technologies as having the potential to improve lives, drive economic growth, and position India as a global leader in science and
The document discusses computers and their key components and capabilities. It notes that a computer can be programmed to perform arithmetic and logical operations, and its sequence of operations can be changed to solve different problems. It also mentions that a computer consists of at least one processing element and some form of memory. The processing element carries out operations and sequencing according to stored instructions. Modern computers based on integrated circuits are millions to billions of times more powerful than early machines and occupy much less space.
This document summarizes a machine learning meetup in Sofia. It discusses trends in cognitive computing and machine learning, including computers that learn, think, interact with humans and other computers. It also outlines enabling technologies for cognitive computing like natural language processing. Specific machine learning tasks like classification, regression and clustering are covered. Challenges in machine learning like data requirements and training time are addressed. The document promotes sharing knowledge and ideas at the open meetup format.
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how AHaH (anti-Hebbian and Hebbian) computing is becoming economically feasible. Traditional computing with the von Neumann architecture requires constant interactions between the processor and the memory (usually DRAM) and improvements in memory access time are occurring at a much slower rate than that of microprocessor speeds. This performance gap is becoming a bottleneck for von Neumann based computers. AHaH computing (and Synaptic computing http://www.slideshare.net/Funk98/neurosynaptic-chips) address this bottleneck in that they use a different architecture that mimics the processing of the brain. AHaH computing has additional differences from von Neumann (and synaptic) architectures in that it reduces the number of interactions between the memory and the processor by combining some aspects of memory on the processing chip. This is done with so-called memristors, which are naturally adaptive systems, and which are experiencing rapid improvements in cost, storage density, and storage capacity. With memristors, widely used pathways become stronger and less widely used pathways become weaker thus facilitating machine learning. Although machine learning can also be done with software, memristors and AHaH computing enables machine learning at the hardware level. The optimism for AHaH computing partly comes from the rapid improvements in memristors, which are rapidly improving the economics of AHaH computing.
The document summarizes a meeting about quantum computers. It discusses:
1. Expectations for quantum computers in overcoming limits of classical computers and addressing modern challenges.
2. The basics of quantum computers, including how they use quantum bits that can represent 0s and 1s simultaneously.
3. Software platforms and algorithms for quantum computing, as well as simulation results showing quantum computers' potential.
4. The current state of quantum computer hardware and development, including types like gate-based and annealing-based models.
Empowering the machine learning evolutionErumShammim
This document summarizes machine learning technologies and their growth. It discusses how big data and increased computing power like GPUs have enabled advances in machine learning. It describes Google's tensor processing unit (TPU) which is an AI chip designed for neural networks. The TPU has increased performance and bandwidth in its second generation. The document also briefly mentions applications and pitfalls of machine learning.
This document discusses key technologies of the future as identified by the Technology Information, Forecasting & Assessment Council (TIFAC). It outlines several technologies including 3D printing, advanced oil and gas exploration, advanced robotics, alternate fuels, artificial intelligence, autonomous vehicles, big data analytics, brain-computer interfaces, cloud technology, digital holography and 3D imaging, energy storage technologies, gamification, immersive virtual reality, internet of things, lab-on-a-chip, quantum computing, real-time translation, semantic web, telemedicine, and wearable devices. TIFAC sees these technologies as having the potential to improve lives, drive economic growth, and position India as a global leader in science and
The document discusses computers and their key components and capabilities. It notes that a computer can be programmed to perform arithmetic and logical operations, and its sequence of operations can be changed to solve different problems. It also mentions that a computer consists of at least one processing element and some form of memory. The processing element carries out operations and sequencing according to stored instructions. Modern computers based on integrated circuits are millions to billions of times more powerful than early machines and occupy much less space.
This document summarizes a machine learning meetup in Sofia. It discusses trends in cognitive computing and machine learning, including computers that learn, think, interact with humans and other computers. It also outlines enabling technologies for cognitive computing like natural language processing. Specific machine learning tasks like classification, regression and clustering are covered. Challenges in machine learning like data requirements and training time are addressed. The document promotes sharing knowledge and ideas at the open meetup format.
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how AHaH (anti-Hebbian and Hebbian) computing is becoming economically feasible. Traditional computing with the von Neumann architecture requires constant interactions between the processor and the memory (usually DRAM) and improvements in memory access time are occurring at a much slower rate than that of microprocessor speeds. This performance gap is becoming a bottleneck for von Neumann based computers. AHaH computing (and Synaptic computing http://www.slideshare.net/Funk98/neurosynaptic-chips) address this bottleneck in that they use a different architecture that mimics the processing of the brain. AHaH computing has additional differences from von Neumann (and synaptic) architectures in that it reduces the number of interactions between the memory and the processor by combining some aspects of memory on the processing chip. This is done with so-called memristors, which are naturally adaptive systems, and which are experiencing rapid improvements in cost, storage density, and storage capacity. With memristors, widely used pathways become stronger and less widely used pathways become weaker thus facilitating machine learning. Although machine learning can also be done with software, memristors and AHaH computing enables machine learning at the hardware level. The optimism for AHaH computing partly comes from the rapid improvements in memristors, which are rapidly improving the economics of AHaH computing.
The document summarizes a meeting about quantum computers. It discusses:
1. Expectations for quantum computers in overcoming limits of classical computers and addressing modern challenges.
2. The basics of quantum computers, including how they use quantum bits that can represent 0s and 1s simultaneously.
3. Software platforms and algorithms for quantum computing, as well as simulation results showing quantum computers' potential.
4. The current state of quantum computer hardware and development, including types like gate-based and annealing-based models.
Things like growing volumes and varieties of available data, cheaper and more powerful computational processing, data storage and large-value predictions that can guide better decisions and smart actions in real time without human intervention are playing critical role in this age. All of these require models that can automatically analyse large complex data and deliver quick accurate results – even on a very large scale. Machine learning plays a significant role in developing these models. The applications of machine learning range from speech and object recognition to analysis and prediction of finance markets. Artificial Neural Network is one of the important algorithms of machine learning that is inspired by the structure and functional aspects of the biological neural networks. In this paper, we discuss the purpose, representation and classification methods for developing hardware for machine learning with the main focus on neural networks. This paper also presents the requirements, design issues and optimization techniques for building hardware architecture of neural networks.
Things like growing volumes and varieties of available data, cheaper and more powerful computational processing, data storage and large-value predictions that can guide better decisions and smart actions inreal time without human intervention are playing critical role in this age. All of these require models thatcan automatically analyse large complex data and deliver quick accurate results – even on a very largescale. Machine learning plays a significant role in developing these models. The applications of machinelearning range from speech and object recognition to analysis and prediction of finance markets. Artificial Neural Network is one of the important algorithms of machine learning that is inspired by the structure and functional aspects of the biological neural networks. In this paper, we discuss the purpose, representationand classification methods for developing hardware for machine learning with the main focus on neuralnetworks. This paper also presents the requirements, design issues and optimization techniques for buildinghardware architecture of neural networks.
The document discusses the Cortex-A11 multicore processor and its components. It describes the processor's architecture including the snoop control unit, accelerator coherence port, generic interrupt controller, advanced bus interface unit, floating point unit, NEON media processing engine, L2 cache controller, program trace macrocell, and memory management unit. The purpose of these components is to provide efficient performance, low power consumption, and scalability for applications such as mobile devices and infotainment systems.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/dec-2016-member-meeting-khronos
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Mark Bünger, Vice President of Research at Lux Research, delivers the presentation "Imaging + AI: Opportunities Inside the Car and Beyond" at the December 2016 Embedded Vision Alliance Member Meeting. Bünger presents his firm’s perspective on how embedded vision will upend the automotive industry.
In our recap of the final day of International Supercomputing 2016, we explore how OpenPOWER members are working in the HPC industry and continue the conversation around cognitive computing, deep learning, and machine learning.
The document defines and provides examples of several key hardware technologies that are important for game platforms, including the central processing unit (CPU), graphics processors, memory (RAM), displays, sound, game storage mediums, interface devices, connectivity, and power supplies. These components allow games to be processed, rendered, stored, and played across various computing and gaming devices. They are essential for enabling the gameplay experiences and virtual worlds that players interact with.
Modern computer network technologies discussed in the document include Internet of Things, Artificial Intelligence, 5G, Edge Computing, Software Defined Networking, Multi Cloud Technology, Quantum Computing, Digital Twin, Nano Network Technology, and Machine Learning. The Internet of Things describes all things connected to the Internet like sensors and smart devices. Artificial Intelligence aims to give human intelligence to machines through technologies like narrow AI and general AI. 5G promises high speeds, reliability and low latency to impact sectors like healthcare. Edge Computing reduces latency and bandwidth by bringing computing closer to data sources. Software Defined Networking abstracts network layers to make networks more agile. Multi Cloud Technology uses multiple public cloud providers for workloads. Quantum Computing performs calculations
The document provides a history of computers from the 1940s to present. It describes how early computers were room-sized and used vacuum tubes, while later generations became smaller, faster, and more reliable using transistors, integrated circuits, and microelectronics. The document also defines key computer components like CPUs, memory, storage, and input/output devices, and how computers are classified by purpose and data type.
BAT40 NVIDIA Stampfli Künstliche Intelligenz, Roboter und autonome Fahrzeuge ...BATbern
Moderne künstliche Intelligenz mit Deep Learning ist bereits
heute schon im Einsatz in verschiedenen Anwendungen.
Sprachsteuerung von Apple mit Siri, Amazon mit Alexa,
autonome Fahrzeuge von Waymo, Tesla, Gesichtserkennung von Facebook sind nur einige bekannte Beispiele aus dem Silicon Valley welche Deep Learning einsetzen.
Der Vortrag zeigt auf was wir von der Technologie erwarten
können und wie Sie unsere Leben beeinflussen wird.
The document discusses IBM PowerAI LMS and DDL. PowerAI LMS allows deep learning models and data to utilize both GPU and system memory for processing. This enables support for larger models and higher resolution data. PowerAI DDL is a communication library that enables distributed deep learning across multiple servers with GPUs. It works with frameworks like TensorFlow to improve scaling efficiency compared to other solutions. A demo is then presented on the performance benefits of PowerAI LMS and DDL.
This document discusses developing an immersive game using computer vision and machine learning techniques with minimal hardware requirements. It proposes replicating the "Squid Game" using a camera, OpenCV for image processing, MediaPipe for human pose estimation, Tkinter for the GUI, and multithreading for performance. The system design involves capturing video frames from the camera, analyzing them using MediaPipe to detect the player's movements, and checking if they follow the game's rules. Tkinter is used to display instructions and get user input while multithreading improves latency. The goal is to demonstrate immersive gaming can be low-cost without advanced sensors or consoles.
Computing power technology refers to the capacity of a computer or computer system to execute complex computations and data processing tasks. The number of calculations or operations a computer or system can perform per second is one common way to express processing speed.
For more info you can visit: https://www.temok.com/blog/computing-power-technology-an-overview/
NVIDIA has continuously reinvented itself over two decades, sparking growth in PC gaming and revolutionizing computer graphics and parallel computing by inventing the GPU in 1999. More recently, GPU computing ignited the era of artificial intelligence. NVIDIA GPUs power today's blockbuster video games and film-quality production values, and their software allows developers to create photorealistic and immersive games. GPU computing provides a path forward as CPU performance growth slows and will deliver a 1,000X speed-up in computing by 2025.
The upsurge of deep learning for computer vision applicationsIJECEIAES
Artificial intelligence (AI) is additionally serving to a brand new breed of corporations disrupt industries from restorative examination to horticulture. Computers can’t nevertheless replace humans, however, they will work superbly taking care of the everyday tangle of our lives. The era is reconstructing big business and has been on the rise in recent years which has grounded with the success of deep learning (DL). Cyber-security, Auto and health industry are three industries innovating with AI and DL technologies and also Banking, retail, finance, robotics, manufacturing. The healthcare industry is one of the earliest adopters of AI and DL. DL accomplishing exceptional dimensions levels of accurateness to the point where DL algorithms can outperform humans at classifying videos & images. The major drivers that caused the breakthrough of deep neural networks are the provision of giant amounts of coaching information, powerful machine infrastructure, and advances in academia. DL is heavily employed in each academe to review intelligence and within the trade-in building intelligent systems to help humans in varied tasks. Thereby DL systems begin to crush not solely classical ways, but additionally, human benchmarks in numerous tasks like image classification, action detection, natural language processing, signal process, and linguistic communication process.
The document discusses the basic components and concepts of computer hardware systems. It describes the evolution of computers from first to fifth generation systems and the major types including supercomputers, mainframes, mini/midrange computers, and microcomputers. It outlines the key components of a basic computer system including input, output, storage, communication and processing devices. Input devices allow data entry, output devices allow data display, storage includes primary memory and secondary devices, and the central processing unit performs computations.
Learn how recent innovation at Google allows you to produce intelligence from IoT data. We will look at some use cases and you will get an overview of the building blocks we use to build truly intelligent IoT solutions in the cloud and on the edge.
A new wave of Artificial intelligence has emerged which has revolutionized the industry/academia.. Much like the web took advantage of existing technologies, this new wave builds on trends such as the decline in the cost of computing hardware, the emergence of the cloud, the fundamental consumerization of the enterprise and, of course, the mobile revolution.
Deep Learning has achieved remarkable breakthroughs, which have, in turn, driven performance improvements across AI components.
Cuda Based Performance Evaluation Of The Computational Efficiency Of The Dct ...acijjournal
Recent advances in computing such as the massively parallel GPUs (Graphical Processing Units),coupled
with the need to store and deliver large quantities of digital data especially images, has brought a number
of challenges for Computer Scientists, the research community and other stakeholders. These challenges,
such as prohibitively large costs to manipulate the digital data amongst others, have been the focus of the
research community in recent years and has led to the investigation of image compression techniques that
can achieve excellent results. One such technique is the Discrete Cosine Transform, which helps separate
an image into parts of differing frequencies and has the advantage of excellent energy-compaction.
This paper investigates the use of the Compute Unified Device Architecture (CUDA) programming model
to implement the DCT based Cordic based Loeffler algorithm for efficient image compression. The
computational efficiency is analyzed and evaluated under both the CPU and GPU. The PSNR (Peak Signal
to Noise Ratio) is used to evaluate image reconstruction quality in this paper. The results are presented
and discussed
The document discusses several topics related to computers and information technology:
1) It summarizes the goals of the Fifth Generation Computer Systems project in Japan to create a new "fifth generation" computer using parallel processing.
2) It explains what an Arithmetic Logic Unit (ALU) is and its important role in the central processing unit (CPU) of performing arithmetic and logical operations.
3) It distinguishes between application software and system software, with application software designed for specific tasks and system software involved in integrating computer capabilities.
This document discusses machine learning and its empowerment through technological advancements. It describes different types of communication technologies from human to human, human to machine, and machine to machine. It then explains why machine learning has advanced recently due to big data, increased computing power through GPUs, and the development of tensor processing units (TPUs) by Google to accelerate neural networks. The document outlines the 1st and 2nd generation of TPUs and their improved processing capabilities. It concludes with a pitfall to avoid which is designing hardware using outdated models.
This document defines and discusses the Islamic concept of shirk, or associating partners with God. It defines shirk as ascribing partners to Allah in his lordship, worship, or attributes. The Quran forbids setting up rivals to Allah. Major shirk involves ascribing something belonging only to Allah, like divinity, to others. Minor shirk includes acts that may lead to major shirk or are described as shirk, but do not reach that level, like emotional attachment to objects for protection without Allah's permission. Inconspicuous shirk occurs when one is dissatisfied with Allah's will.
Things like growing volumes and varieties of available data, cheaper and more powerful computational processing, data storage and large-value predictions that can guide better decisions and smart actions in real time without human intervention are playing critical role in this age. All of these require models that can automatically analyse large complex data and deliver quick accurate results – even on a very large scale. Machine learning plays a significant role in developing these models. The applications of machine learning range from speech and object recognition to analysis and prediction of finance markets. Artificial Neural Network is one of the important algorithms of machine learning that is inspired by the structure and functional aspects of the biological neural networks. In this paper, we discuss the purpose, representation and classification methods for developing hardware for machine learning with the main focus on neural networks. This paper also presents the requirements, design issues and optimization techniques for building hardware architecture of neural networks.
Things like growing volumes and varieties of available data, cheaper and more powerful computational processing, data storage and large-value predictions that can guide better decisions and smart actions inreal time without human intervention are playing critical role in this age. All of these require models thatcan automatically analyse large complex data and deliver quick accurate results – even on a very largescale. Machine learning plays a significant role in developing these models. The applications of machinelearning range from speech and object recognition to analysis and prediction of finance markets. Artificial Neural Network is one of the important algorithms of machine learning that is inspired by the structure and functional aspects of the biological neural networks. In this paper, we discuss the purpose, representationand classification methods for developing hardware for machine learning with the main focus on neuralnetworks. This paper also presents the requirements, design issues and optimization techniques for buildinghardware architecture of neural networks.
The document discusses the Cortex-A11 multicore processor and its components. It describes the processor's architecture including the snoop control unit, accelerator coherence port, generic interrupt controller, advanced bus interface unit, floating point unit, NEON media processing engine, L2 cache controller, program trace macrocell, and memory management unit. The purpose of these components is to provide efficient performance, low power consumption, and scalability for applications such as mobile devices and infotainment systems.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/dec-2016-member-meeting-khronos
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Mark Bünger, Vice President of Research at Lux Research, delivers the presentation "Imaging + AI: Opportunities Inside the Car and Beyond" at the December 2016 Embedded Vision Alliance Member Meeting. Bünger presents his firm’s perspective on how embedded vision will upend the automotive industry.
In our recap of the final day of International Supercomputing 2016, we explore how OpenPOWER members are working in the HPC industry and continue the conversation around cognitive computing, deep learning, and machine learning.
The document defines and provides examples of several key hardware technologies that are important for game platforms, including the central processing unit (CPU), graphics processors, memory (RAM), displays, sound, game storage mediums, interface devices, connectivity, and power supplies. These components allow games to be processed, rendered, stored, and played across various computing and gaming devices. They are essential for enabling the gameplay experiences and virtual worlds that players interact with.
Modern computer network technologies discussed in the document include Internet of Things, Artificial Intelligence, 5G, Edge Computing, Software Defined Networking, Multi Cloud Technology, Quantum Computing, Digital Twin, Nano Network Technology, and Machine Learning. The Internet of Things describes all things connected to the Internet like sensors and smart devices. Artificial Intelligence aims to give human intelligence to machines through technologies like narrow AI and general AI. 5G promises high speeds, reliability and low latency to impact sectors like healthcare. Edge Computing reduces latency and bandwidth by bringing computing closer to data sources. Software Defined Networking abstracts network layers to make networks more agile. Multi Cloud Technology uses multiple public cloud providers for workloads. Quantum Computing performs calculations
The document provides a history of computers from the 1940s to present. It describes how early computers were room-sized and used vacuum tubes, while later generations became smaller, faster, and more reliable using transistors, integrated circuits, and microelectronics. The document also defines key computer components like CPUs, memory, storage, and input/output devices, and how computers are classified by purpose and data type.
BAT40 NVIDIA Stampfli Künstliche Intelligenz, Roboter und autonome Fahrzeuge ...BATbern
Moderne künstliche Intelligenz mit Deep Learning ist bereits
heute schon im Einsatz in verschiedenen Anwendungen.
Sprachsteuerung von Apple mit Siri, Amazon mit Alexa,
autonome Fahrzeuge von Waymo, Tesla, Gesichtserkennung von Facebook sind nur einige bekannte Beispiele aus dem Silicon Valley welche Deep Learning einsetzen.
Der Vortrag zeigt auf was wir von der Technologie erwarten
können und wie Sie unsere Leben beeinflussen wird.
The document discusses IBM PowerAI LMS and DDL. PowerAI LMS allows deep learning models and data to utilize both GPU and system memory for processing. This enables support for larger models and higher resolution data. PowerAI DDL is a communication library that enables distributed deep learning across multiple servers with GPUs. It works with frameworks like TensorFlow to improve scaling efficiency compared to other solutions. A demo is then presented on the performance benefits of PowerAI LMS and DDL.
This document discusses developing an immersive game using computer vision and machine learning techniques with minimal hardware requirements. It proposes replicating the "Squid Game" using a camera, OpenCV for image processing, MediaPipe for human pose estimation, Tkinter for the GUI, and multithreading for performance. The system design involves capturing video frames from the camera, analyzing them using MediaPipe to detect the player's movements, and checking if they follow the game's rules. Tkinter is used to display instructions and get user input while multithreading improves latency. The goal is to demonstrate immersive gaming can be low-cost without advanced sensors or consoles.
Computing power technology refers to the capacity of a computer or computer system to execute complex computations and data processing tasks. The number of calculations or operations a computer or system can perform per second is one common way to express processing speed.
For more info you can visit: https://www.temok.com/blog/computing-power-technology-an-overview/
NVIDIA has continuously reinvented itself over two decades, sparking growth in PC gaming and revolutionizing computer graphics and parallel computing by inventing the GPU in 1999. More recently, GPU computing ignited the era of artificial intelligence. NVIDIA GPUs power today's blockbuster video games and film-quality production values, and their software allows developers to create photorealistic and immersive games. GPU computing provides a path forward as CPU performance growth slows and will deliver a 1,000X speed-up in computing by 2025.
The upsurge of deep learning for computer vision applicationsIJECEIAES
Artificial intelligence (AI) is additionally serving to a brand new breed of corporations disrupt industries from restorative examination to horticulture. Computers can’t nevertheless replace humans, however, they will work superbly taking care of the everyday tangle of our lives. The era is reconstructing big business and has been on the rise in recent years which has grounded with the success of deep learning (DL). Cyber-security, Auto and health industry are three industries innovating with AI and DL technologies and also Banking, retail, finance, robotics, manufacturing. The healthcare industry is one of the earliest adopters of AI and DL. DL accomplishing exceptional dimensions levels of accurateness to the point where DL algorithms can outperform humans at classifying videos & images. The major drivers that caused the breakthrough of deep neural networks are the provision of giant amounts of coaching information, powerful machine infrastructure, and advances in academia. DL is heavily employed in each academe to review intelligence and within the trade-in building intelligent systems to help humans in varied tasks. Thereby DL systems begin to crush not solely classical ways, but additionally, human benchmarks in numerous tasks like image classification, action detection, natural language processing, signal process, and linguistic communication process.
The document discusses the basic components and concepts of computer hardware systems. It describes the evolution of computers from first to fifth generation systems and the major types including supercomputers, mainframes, mini/midrange computers, and microcomputers. It outlines the key components of a basic computer system including input, output, storage, communication and processing devices. Input devices allow data entry, output devices allow data display, storage includes primary memory and secondary devices, and the central processing unit performs computations.
Learn how recent innovation at Google allows you to produce intelligence from IoT data. We will look at some use cases and you will get an overview of the building blocks we use to build truly intelligent IoT solutions in the cloud and on the edge.
A new wave of Artificial intelligence has emerged which has revolutionized the industry/academia.. Much like the web took advantage of existing technologies, this new wave builds on trends such as the decline in the cost of computing hardware, the emergence of the cloud, the fundamental consumerization of the enterprise and, of course, the mobile revolution.
Deep Learning has achieved remarkable breakthroughs, which have, in turn, driven performance improvements across AI components.
Cuda Based Performance Evaluation Of The Computational Efficiency Of The Dct ...acijjournal
Recent advances in computing such as the massively parallel GPUs (Graphical Processing Units),coupled
with the need to store and deliver large quantities of digital data especially images, has brought a number
of challenges for Computer Scientists, the research community and other stakeholders. These challenges,
such as prohibitively large costs to manipulate the digital data amongst others, have been the focus of the
research community in recent years and has led to the investigation of image compression techniques that
can achieve excellent results. One such technique is the Discrete Cosine Transform, which helps separate
an image into parts of differing frequencies and has the advantage of excellent energy-compaction.
This paper investigates the use of the Compute Unified Device Architecture (CUDA) programming model
to implement the DCT based Cordic based Loeffler algorithm for efficient image compression. The
computational efficiency is analyzed and evaluated under both the CPU and GPU. The PSNR (Peak Signal
to Noise Ratio) is used to evaluate image reconstruction quality in this paper. The results are presented
and discussed
The document discusses several topics related to computers and information technology:
1) It summarizes the goals of the Fifth Generation Computer Systems project in Japan to create a new "fifth generation" computer using parallel processing.
2) It explains what an Arithmetic Logic Unit (ALU) is and its important role in the central processing unit (CPU) of performing arithmetic and logical operations.
3) It distinguishes between application software and system software, with application software designed for specific tasks and system software involved in integrating computer capabilities.
Similar to Empowering Machine Learning Evolution (20)
This document discusses machine learning and its empowerment through technological advancements. It describes different types of communication technologies from human to human, human to machine, and machine to machine. It then explains why machine learning has advanced recently due to big data, increased computing power through GPUs, and the development of tensor processing units (TPUs) by Google to accelerate neural networks. The document outlines the 1st and 2nd generation of TPUs and their improved processing capabilities. It concludes with a pitfall to avoid which is designing hardware using outdated models.
This document defines and discusses the Islamic concept of shirk, or associating partners with God. It defines shirk as ascribing partners to Allah in his lordship, worship, or attributes. The Quran forbids setting up rivals to Allah. Major shirk involves ascribing something belonging only to Allah, like divinity, to others. Minor shirk includes acts that may lead to major shirk or are described as shirk, but do not reach that level, like emotional attachment to objects for protection without Allah's permission. Inconspicuous shirk occurs when one is dissatisfied with Allah's will.
Preprocessor directives are instructions to the compiler that begin with the # character and are processed before compilation. Common preprocessor directives include #include, which is used to include header files, and #define, which assigns names to constants, statements, or expressions that can be used repeatedly in a program. Preprocessor directives allow programs to be preprocessed before compilation.
a description of the three main loops in all the programming languages along with their definition,syntax,flowchat and examples.
note: programs are written in C language
The presentation compares the Android, iOS, and Windows mobile platforms. It discusses the history and development of each platform, including their origins, versions, and development tools. A framework comparison covers features like local storage, multitasking, maps, audio/video, encryption, and push notifications. The presentation analyzes the advantages and disadvantages of each platform in areas like performance, applications, security, diversity, and market share. It concludes by recommending Android for applications, Windows for work/organization, and iOS for speed, safety and design.
The Chinese New Year, also known as the Spring Festival, is an important 15-day holiday in Chinese culture that is celebrated with family reunions, firecrackers, dragon dances, and the lighting of lanterns. Special foods like dumplings, rice balls, fish, and noodles are eaten for their symbolic meanings of togetherness, prosperity, and good fortune in the coming year. Traditional activities over the 15 days include cleaning the house, pasting couplets, family dinners, giving red envelopes, watching galas, and lantern festivals.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
2. Communications
Communication theory states that
communication involves a sender and a
receiver (or receivers) conveying
information through a communication
channel.
7. Big Data
One of the important reasons is the
volume of available data. Terabyte-
sized Big Data can be easily
accessed with few clicks.
Computing Power
The second reason is the advanced
computing power, especially
using Graphics Processing Unit
(GPU). GPU is a
specialized electronic
circuit designed to rapidly
manipulate and alter memory to
accelerate the creation of images in
a frame buffer.
8. Google Tensor Processing Unit(TPU)
A tensor processing unit (TPU) is an AI accelerator
application-specific integrated circuit (ASIC)
developed by Google specifically for neural network
machine learning.
1st Generation
The tensor processing unit was announced in 2016
at Google I/O.The chip has been specifically
designed for Google's Tensor Flow framework, a
symbolic math library which is used for machine
learning applications such as neural networks.
9. 2nd Generation
The second generation TPU was
announced in May 2017.Google stated
the first generation TPU design was
memory bandwidth limited, and using 16
GB of High Bandwidth Memory, In the
second generation design increased
bandwidth to 600GB/s and performance
to 45 TFLOPS.