This document introduces autoencoders and their applications in IoT analytics for eldercare. It provides an overview of neural network models including autoencoders and how they can be used for dimensionality reduction, anomaly detection, and generating new outputs. It then discusses a case study where autoencoders are used to analyze sensor data from smart homes and identify activities of daily living (ADLs) like cooking, bathing, and sleeping based on patterns of sensor activations over time.
This document provides an overview of fingerprint recognition as a biometric authentication method. It discusses the uniqueness of fingerprints and how they remain unchanged throughout a person's life. The document then describes how optical fingerprint sensors capture digital images of fingerprints using visible light. It explains some of the advantages and disadvantages of this sensing technique. Finally, it provides high-level descriptions of the components used in a fingerprint recognition circuit, including the microcontroller, reset circuitry, crystals, and an LCD display for output. The goal of the circuit is to authenticate a person's identity by matching their fingerprint to a stored template.
A talk I gave to Hackware v0.7, v0.8, Hackers & Painters, NUS Hackers, One Maker Group and iOS Dev Scout to introduce the audience to basic Bluetooth Low Energy concepts followed by code explanations.
Part 1 of my presentation at Hackware v0.7 (Arduino and Android only) can be seen here. https://www.youtube.com/watch?v=pNnwXPatzjc
Part 2 (Raspberry Pi, iOS and BLE Sniffer) can be seen here. https://www.youtube.com/watch?v=UDNkrlfW9Sg
The code is available here. https://github.com/yeokm1/intro-to-ble
IRJET- New Generation Multilevel based Atm Security SystemIRJET Journal
This document describes a new multi-level security system for ATMs using face recognition. The system detects a user's face when accessing an ATM card and sends the photo to the authorized card owner. This allows the owner to identify if an unauthorized person is using their lost or stolen card. The system provides higher security than existing ATM security by verifying the user's identity using facial recognition each time the card is used.
The document discusses hardware evolution, which applies evolutionary techniques to hardware design and synthesis. It is not just implementing evolutionary algorithms in hardware. Hardware evolution can optimize hardware designs, map designs to programmable chips like FPGAs, and even evolve digital circuits directly on reconfigurable hardware. The document provides examples of how evolution can be used to optimize adder circuits, image compression algorithms, and other applications implemented on reconfigurable hardware. It also discusses constraints and evaluation strategies in hardware evolution.
Implementation of 32 Bit Binary Floating Point Adder Using IEEE 754 Single Pr...iosrjce
Field Programmable Gate Arrays (FPGA) are increasingly being used to design high- end
computationally intense microprocessors capable of handling both fixed and floating- point mathematical
operations. Addition is the most complex operation in a floating-point unit and offers major delay while taking
significant area. Over the years, the VLSI community has developed many floating-point adder algorithms
mainly aimed to reduce the overall latency. The Objective of this paper to implement the 32 bit binary floating
point adder with minimum time. Floating point numbers are used in various applications such as medical
imaging, radar, telecommunications Etc. Here pipelined architecture is used in order to increase the
performance and the design is achieved to increase the operating frequency. The logic is designed using VHDL.
This paper discusses in detail the best possible FPGA implementation will act as an important design resource.
The performance criterion is latency in all the cases. The algorithms are compared for overall latency, area,
and levels of logic and analyzed specifically for one of the latest FPGA architectures provided by Xilinx.
Bayesian Autoencoders (BAE) & Honest Thoughts on research Bang Xiang Yong
1. The document discusses Bayesian Autoencoders (BAE), including how the author came to study them and what they aim to achieve.
2. It explains that BAEs aim to quantify uncertainty in deep learning models to better handle out-of-distribution data, which standard autoencoders cannot do.
3. The author hopes to develop BAEs and related tools further to create research products that can help companies detect anomalies and outliers in real-world datasets.
The document discusses design for test (DFT) techniques. It explains that DFT aims to improve the testability of chip designs by adding mechanisms to control and observe internal nodes for manufacturing testing. This allows testing of each block or component on the chip to identify defective parts. Specifically, it discusses using scan chains to test combinational logic, and techniques like MBIST and boundary scan for testing memories and I/O, respectively. The goal of DFT is to effectively test designs at the component level to improve quality and yield.
This document describes a digital IC tester project created by students to test integrated circuits (ICs). The tester uses two microcontrollers - one to interface with the keypad and LCD display, and one to test the ICs by providing input signals and checking the output. It can test common ICs like logic gates, counters, multiplexers and shift registers. The students developed the circuit on a breadboard first, then a printed circuit board. They added a keypad and LCD for user-friendly input and output display. The document outlines the working, components that can be tested, problems faced during development and potential future extensions.
This document provides an overview of fingerprint recognition as a biometric authentication method. It discusses the uniqueness of fingerprints and how they remain unchanged throughout a person's life. The document then describes how optical fingerprint sensors capture digital images of fingerprints using visible light. It explains some of the advantages and disadvantages of this sensing technique. Finally, it provides high-level descriptions of the components used in a fingerprint recognition circuit, including the microcontroller, reset circuitry, crystals, and an LCD display for output. The goal of the circuit is to authenticate a person's identity by matching their fingerprint to a stored template.
A talk I gave to Hackware v0.7, v0.8, Hackers & Painters, NUS Hackers, One Maker Group and iOS Dev Scout to introduce the audience to basic Bluetooth Low Energy concepts followed by code explanations.
Part 1 of my presentation at Hackware v0.7 (Arduino and Android only) can be seen here. https://www.youtube.com/watch?v=pNnwXPatzjc
Part 2 (Raspberry Pi, iOS and BLE Sniffer) can be seen here. https://www.youtube.com/watch?v=UDNkrlfW9Sg
The code is available here. https://github.com/yeokm1/intro-to-ble
IRJET- New Generation Multilevel based Atm Security SystemIRJET Journal
This document describes a new multi-level security system for ATMs using face recognition. The system detects a user's face when accessing an ATM card and sends the photo to the authorized card owner. This allows the owner to identify if an unauthorized person is using their lost or stolen card. The system provides higher security than existing ATM security by verifying the user's identity using facial recognition each time the card is used.
The document discusses hardware evolution, which applies evolutionary techniques to hardware design and synthesis. It is not just implementing evolutionary algorithms in hardware. Hardware evolution can optimize hardware designs, map designs to programmable chips like FPGAs, and even evolve digital circuits directly on reconfigurable hardware. The document provides examples of how evolution can be used to optimize adder circuits, image compression algorithms, and other applications implemented on reconfigurable hardware. It also discusses constraints and evaluation strategies in hardware evolution.
Implementation of 32 Bit Binary Floating Point Adder Using IEEE 754 Single Pr...iosrjce
Field Programmable Gate Arrays (FPGA) are increasingly being used to design high- end
computationally intense microprocessors capable of handling both fixed and floating- point mathematical
operations. Addition is the most complex operation in a floating-point unit and offers major delay while taking
significant area. Over the years, the VLSI community has developed many floating-point adder algorithms
mainly aimed to reduce the overall latency. The Objective of this paper to implement the 32 bit binary floating
point adder with minimum time. Floating point numbers are used in various applications such as medical
imaging, radar, telecommunications Etc. Here pipelined architecture is used in order to increase the
performance and the design is achieved to increase the operating frequency. The logic is designed using VHDL.
This paper discusses in detail the best possible FPGA implementation will act as an important design resource.
The performance criterion is latency in all the cases. The algorithms are compared for overall latency, area,
and levels of logic and analyzed specifically for one of the latest FPGA architectures provided by Xilinx.
Bayesian Autoencoders (BAE) & Honest Thoughts on research Bang Xiang Yong
1. The document discusses Bayesian Autoencoders (BAE), including how the author came to study them and what they aim to achieve.
2. It explains that BAEs aim to quantify uncertainty in deep learning models to better handle out-of-distribution data, which standard autoencoders cannot do.
3. The author hopes to develop BAEs and related tools further to create research products that can help companies detect anomalies and outliers in real-world datasets.
The document discusses design for test (DFT) techniques. It explains that DFT aims to improve the testability of chip designs by adding mechanisms to control and observe internal nodes for manufacturing testing. This allows testing of each block or component on the chip to identify defective parts. Specifically, it discusses using scan chains to test combinational logic, and techniques like MBIST and boundary scan for testing memories and I/O, respectively. The goal of DFT is to effectively test designs at the component level to improve quality and yield.
This document describes a digital IC tester project created by students to test integrated circuits (ICs). The tester uses two microcontrollers - one to interface with the keypad and LCD display, and one to test the ICs by providing input signals and checking the output. It can test common ICs like logic gates, counters, multiplexers and shift registers. The students developed the circuit on a breadboard first, then a printed circuit board. They added a keypad and LCD for user-friendly input and output display. The document outlines the working, components that can be tested, problems faced during development and potential future extensions.
This document describes a digital alarm clock designed and implemented on an Artix7 FPGA development board using Verilog HDL. The clock displays time in hours, minutes and seconds using 8 seven-segment displays and blinks the decimal point LED between hour and minute display. It allows the user to set the current time and alarm time using buttons and has functionality for clock setting, alarm setting and an alarm alert indicator LED or sound. The design was tested successfully using hardware on the FPGA board and some minor issues were addressed. Future work proposed includes modifying the clock format and adding a date display.
IRJET - New Generation Multilevel based Atm Security SystemIRJET Journal
This document proposes a new multi-level security system for ATMs using face recognition. The system would detect the face of any unauthorized person using an ATM card and send their photo to the authorized card holder. This allows card holders to easily identify who accessed their card if it was lost or stolen. The system uses an Arduino, RFID reader instead of an ATM card, and OpenCV with Python for face detection and recognition. If an unauthorized face is detected, an alarm would sound and their photo would be emailed. This new system provides improved authentication and security over existing ATM technologies.
This document discusses writing applications for Scylla and optimizing disk access. It presents two options for running analytics on sensor data: querying individual partitions or using range scans. Testing on sample data shows range scans greatly reduce disk I/O and bytes read. The document also outlines new CQL features in Scylla 3.1 and 3.2 like GROUP BY, PER PARTITION LIMIT, and BYPASS CACHE that can improve performance. Optimized full scans are generally better than aggregated single partition scans when doing analytics workloads that access data from disk.
This document provides legal notices and disclaimers for an informational presentation by Intel. It states that the presentation is for informational purposes only and that Intel makes no warranties. It also notes that Intel technologies' features and benefits depend on system configuration. Finally, it specifies that the sample source code in the presentation is released under the Intel Sample Source Code License Agreement and that Intel and its logo are trademarks.
This document provides information about a workshop on FIO (Funnel I/O), which is a hardware and software platform that allows physical computing using Arduino, XBee radio modules, and the Funnel programming environment. The workshop agenda includes primers on Arduino, XBee radios, and using the Funnel visual programming interface. Participants will learn how to connect Arduino-based sensors and actuators over XBee radios and control them remotely from a PC using Funnel. The document lists the required materials and provides code examples for basic input/output tasks with FIO.
The document discusses artificial neural networks and backpropagation. It provides an overview of backpropagation algorithms, including how they were developed over time, the basic methodology of propagating errors backwards, and typical network architectures. It also gives examples of applying backpropagation to problems like robotics, space robots, handwritten digit recognition, and face recognition.
This document discusses third party patches for MySQL that provide quick wins and new features. It summarizes five such patches: 1) Slow query filtering which helps identify expensive queries, 2) Index statistics which helps determine unused indexes, 3) An InnoDB dictionary limit which constrains memory usage, 4) A global long query time setting, and 5) A "fix" for InnoDB group commit performance regressions in MySQL 5.0. The document encourages using third party patches to gain features and improvements not yet available in the MySQL core.
The document describes the design and implementation of a real-time weather monitoring system using Internet of Things technologies. The system gathers data on temperature, humidity, rainfall, and light levels from various sensors and sends it in real-time to a local server and Blynk application. The weather station allows users to access current and historical weather data for analysis and forecasting. It uses low-cost sensors and microcontrollers to provide an affordable solution for remote environmental monitoring.
IEC 61131-9 is an international standard for IO-Link, a digital communication protocol for sensors and actuators. IO-Link allows bi-directional communication between a sensor and PLC, enabling features like sensor diagnostics, configuration, and process data beyond just on/off signals. IO-Link uses the same M12, M8, and M5 connectors as traditional sensors, providing compatibility while adding digital capabilities. The IO-Link standard provides for interoperability between sensors, masters, and PLCs from any manufacturer.
LST Toolkit: Exfiltration Over Sound, Light, TouchDimitry Snezhkov
The document discusses offensive and defensive strategies around exfiltrating sensitive data from secured environments. It describes observing defenses that focus on network-level exfiltration and lack behavioral context. Custom threat modeling and solutions may be needed. Tactics discussed include exploiting existing facilities, avoiding defenses, and transforming data to bypass monitoring. The document also outlines fictional scenarios where innovative techniques like encoding data in screen pixels or QR codes are used to exfiltrate information despite strengthened defenses.
Introduction to AIoT & TinyML - with ArduinoAndri Yadi
On last March 21, 2020, we participated in worldwide Arduino Day 2020 and organized the online event for Bandung, Indonesia. This is the deck I delivered for my talk and demo.
Deep learning uses neural networks with many hidden layers to learn representations of data with multiple levels of abstraction. It has been shown to outperform simpler models with fewer layers on complex tasks like image and speech recognition. Deep learning works by defining a set of candidate functions (neural networks) and using gradient descent to optimize the network parameters to minimize loss on training data. Deeper networks with more parameters generally perform better but require large datasets and computational resources to train effectively.
This document provides information about various digital circuits including half adder, full adder, encoder, decoder, multiplexer, demultiplexer, seven segment display circuit, clock, flip flop, integrated circuit and more. It defines combinational and sequential circuits. It describes half adder, full adder, encoder, decoder, multiplexer, demultiplexer and seven segment display circuits. It also explains clock, flip flops including SR, D, JK flip flops, integrated circuits and definitions.
04 accelerating dl inference with (open)capi and posit numbersYutaka Kawai
This was presented by Louis Ledoux and Marc Casas at OpenPOWER summit EU 2019. The original one is uploaded at:
https://static.sched.com/hosted_files/opeu19/1a/presentation_louis_ledoux_posit.pdf
This document discusses real-time image processing. It begins with an introduction and definitions of real-time and non-real-time processing. It then discusses the requirements for a real-time image processing platform, including high resolution/frame rate video input and low latency. The document outlines some advantages of real-time image processing such as immediate results and automation. It then provides an overview of an object detection system using Viola-Jones detection with integral images, AdaBoost learning, and a cascade classifier structure. Experimental results show the cascade classifier can detect faces in real-time.
A separately excited dc motor is driven from a 240v, 50HZ supply via a HC
SCR-bridge with a fly-wheel diode. The motor has an armature resistance
1Ω, an armature voltage constant Kv of 0.8 V. s/rad. The field current is
constant. Assume steady armature current. Determine the armature current
and torque for 1600 rpm and a firing angle delay of a) 30° b) 60
This document provides information about integrating a keyboard and LCD display with a microcontroller and creating a CID calculator project. It includes:
1) Block diagrams and classifications of microprocessors and microcontrollers.
2) Instructions on connecting a 4x4 keyboard to a microcontroller port and reading button presses.
3) Details on initializing and writing text and numbers to a 16x2 LCD display connected to a microcontroller.
4) An overview of the steps needed to program a microcontroller and create a CID calculator, including required components and sample code.
Introduction to Digital Electronics & What we will study.pptGauravKumarDas5
This document provides details about the ECE 213 Digital Electronics course. It includes information about the course instructor, textbook, assessment breakdown, topics to be covered in each unit including binary number systems, logic gates, combinational and sequential logic systems, memory and applications. The objectives are to understand digital concepts and apply them to analyze and design basic digital circuits and systems. The future scope of digital electronics is also highlighted due to advantages like reduced size, improved performance and secure data transmission using VLSI technology.
This document describes a digital alarm clock designed and implemented on an Artix7 FPGA development board using Verilog HDL. The clock displays time in hours, minutes and seconds using 8 seven-segment displays and blinks the decimal point LED between hour and minute display. It allows the user to set the current time and alarm time using buttons and has functionality for clock setting, alarm setting and an alarm alert indicator LED or sound. The design was tested successfully using hardware on the FPGA board and some minor issues were addressed. Future work proposed includes modifying the clock format and adding a date display.
IRJET - New Generation Multilevel based Atm Security SystemIRJET Journal
This document proposes a new multi-level security system for ATMs using face recognition. The system would detect the face of any unauthorized person using an ATM card and send their photo to the authorized card holder. This allows card holders to easily identify who accessed their card if it was lost or stolen. The system uses an Arduino, RFID reader instead of an ATM card, and OpenCV with Python for face detection and recognition. If an unauthorized face is detected, an alarm would sound and their photo would be emailed. This new system provides improved authentication and security over existing ATM technologies.
This document discusses writing applications for Scylla and optimizing disk access. It presents two options for running analytics on sensor data: querying individual partitions or using range scans. Testing on sample data shows range scans greatly reduce disk I/O and bytes read. The document also outlines new CQL features in Scylla 3.1 and 3.2 like GROUP BY, PER PARTITION LIMIT, and BYPASS CACHE that can improve performance. Optimized full scans are generally better than aggregated single partition scans when doing analytics workloads that access data from disk.
This document provides legal notices and disclaimers for an informational presentation by Intel. It states that the presentation is for informational purposes only and that Intel makes no warranties. It also notes that Intel technologies' features and benefits depend on system configuration. Finally, it specifies that the sample source code in the presentation is released under the Intel Sample Source Code License Agreement and that Intel and its logo are trademarks.
This document provides information about a workshop on FIO (Funnel I/O), which is a hardware and software platform that allows physical computing using Arduino, XBee radio modules, and the Funnel programming environment. The workshop agenda includes primers on Arduino, XBee radios, and using the Funnel visual programming interface. Participants will learn how to connect Arduino-based sensors and actuators over XBee radios and control them remotely from a PC using Funnel. The document lists the required materials and provides code examples for basic input/output tasks with FIO.
The document discusses artificial neural networks and backpropagation. It provides an overview of backpropagation algorithms, including how they were developed over time, the basic methodology of propagating errors backwards, and typical network architectures. It also gives examples of applying backpropagation to problems like robotics, space robots, handwritten digit recognition, and face recognition.
This document discusses third party patches for MySQL that provide quick wins and new features. It summarizes five such patches: 1) Slow query filtering which helps identify expensive queries, 2) Index statistics which helps determine unused indexes, 3) An InnoDB dictionary limit which constrains memory usage, 4) A global long query time setting, and 5) A "fix" for InnoDB group commit performance regressions in MySQL 5.0. The document encourages using third party patches to gain features and improvements not yet available in the MySQL core.
The document describes the design and implementation of a real-time weather monitoring system using Internet of Things technologies. The system gathers data on temperature, humidity, rainfall, and light levels from various sensors and sends it in real-time to a local server and Blynk application. The weather station allows users to access current and historical weather data for analysis and forecasting. It uses low-cost sensors and microcontrollers to provide an affordable solution for remote environmental monitoring.
IEC 61131-9 is an international standard for IO-Link, a digital communication protocol for sensors and actuators. IO-Link allows bi-directional communication between a sensor and PLC, enabling features like sensor diagnostics, configuration, and process data beyond just on/off signals. IO-Link uses the same M12, M8, and M5 connectors as traditional sensors, providing compatibility while adding digital capabilities. The IO-Link standard provides for interoperability between sensors, masters, and PLCs from any manufacturer.
LST Toolkit: Exfiltration Over Sound, Light, TouchDimitry Snezhkov
The document discusses offensive and defensive strategies around exfiltrating sensitive data from secured environments. It describes observing defenses that focus on network-level exfiltration and lack behavioral context. Custom threat modeling and solutions may be needed. Tactics discussed include exploiting existing facilities, avoiding defenses, and transforming data to bypass monitoring. The document also outlines fictional scenarios where innovative techniques like encoding data in screen pixels or QR codes are used to exfiltrate information despite strengthened defenses.
Introduction to AIoT & TinyML - with ArduinoAndri Yadi
On last March 21, 2020, we participated in worldwide Arduino Day 2020 and organized the online event for Bandung, Indonesia. This is the deck I delivered for my talk and demo.
Deep learning uses neural networks with many hidden layers to learn representations of data with multiple levels of abstraction. It has been shown to outperform simpler models with fewer layers on complex tasks like image and speech recognition. Deep learning works by defining a set of candidate functions (neural networks) and using gradient descent to optimize the network parameters to minimize loss on training data. Deeper networks with more parameters generally perform better but require large datasets and computational resources to train effectively.
This document provides information about various digital circuits including half adder, full adder, encoder, decoder, multiplexer, demultiplexer, seven segment display circuit, clock, flip flop, integrated circuit and more. It defines combinational and sequential circuits. It describes half adder, full adder, encoder, decoder, multiplexer, demultiplexer and seven segment display circuits. It also explains clock, flip flops including SR, D, JK flip flops, integrated circuits and definitions.
04 accelerating dl inference with (open)capi and posit numbersYutaka Kawai
This was presented by Louis Ledoux and Marc Casas at OpenPOWER summit EU 2019. The original one is uploaded at:
https://static.sched.com/hosted_files/opeu19/1a/presentation_louis_ledoux_posit.pdf
This document discusses real-time image processing. It begins with an introduction and definitions of real-time and non-real-time processing. It then discusses the requirements for a real-time image processing platform, including high resolution/frame rate video input and low latency. The document outlines some advantages of real-time image processing such as immediate results and automation. It then provides an overview of an object detection system using Viola-Jones detection with integral images, AdaBoost learning, and a cascade classifier structure. Experimental results show the cascade classifier can detect faces in real-time.
A separately excited dc motor is driven from a 240v, 50HZ supply via a HC
SCR-bridge with a fly-wheel diode. The motor has an armature resistance
1Ω, an armature voltage constant Kv of 0.8 V. s/rad. The field current is
constant. Assume steady armature current. Determine the armature current
and torque for 1600 rpm and a firing angle delay of a) 30° b) 60
This document provides information about integrating a keyboard and LCD display with a microcontroller and creating a CID calculator project. It includes:
1) Block diagrams and classifications of microprocessors and microcontrollers.
2) Instructions on connecting a 4x4 keyboard to a microcontroller port and reading button presses.
3) Details on initializing and writing text and numbers to a 16x2 LCD display connected to a microcontroller.
4) An overview of the steps needed to program a microcontroller and create a CID calculator, including required components and sample code.
Introduction to Digital Electronics & What we will study.pptGauravKumarDas5
This document provides details about the ECE 213 Digital Electronics course. It includes information about the course instructor, textbook, assessment breakdown, topics to be covered in each unit including binary number systems, logic gates, combinational and sequential logic systems, memory and applications. The objectives are to understand digital concepts and apply them to analyze and design basic digital circuits and systems. The future scope of digital electronics is also highlighted due to advantages like reduced size, improved performance and secure data transmission using VLSI technology.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
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/
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
2. Neural Network Model
3/19/2019 Copyright Scott N. Gerard 2019 2
Input layer
.6
.1 .9
.5
.4
.8
.6
.3 .3
.7
.3 .1
2 hidden layers Output layer
• Layers are fully connected
• Each edge contains a weight
• Final answer is output neuron with highest value
x f1(x) f2(f1(x)) f3(f2(f1(x))) • Each function/layer fi is a non-linear function
width
depth
3. Neural Network Training and Inference
3/19/2019 Copyright Scott N. Gerard 2019 3
Input layer
Error = label – prediction
Labeled
Input
Backpropagation
Feed forward Label (ground truth)
.6
.1 .9
.5
.4
.8
.6
.3 .3
.7
.3 .1
2 hidden layers Output layer
• Supervised learning
• epoch = 1 fwd+bwd pass over all training
• mini-batch = 1 fwd+bwd pass over fraction of training
• # iterations = training size / mini-batch size
Feed forward
Training
Phase
Inference
Phase
NNmodel
(weights)
Unseen
Input Prediction
Train model
Use model
4. Bad Autoencoder
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input
feature vector
same input
feature vector
• Unsupervised learning
• Reconstruction loss
=sum (output-input)2
identity(x)
5. Autoencoder
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encoder
compressor
decoder
generator
input
feature vector
same input
feature vector
• Unsupervised learning
• Compresses input
• Learn important features
• NLP’s word2vec is latent space
• ½-hour sit-coms 😉
• How much compression?
• Auto-generate new sit-coms?
“bottleneck”
coding
latent space
f(x) “f -1”(x)
6. MNIST dataset (sample)
Autoencoder
Autoencoder Learns Handwritten Digits
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• 784 neurons in input layer (=28x28 pixels)
• 256 neurons in hidden layer
• 128 neurons in latent space (middle layer)
• 256 neurons in hidden layer
• 10 neurons in output layer (1 for each digit)
• 30,000 MNIST training images
• Batch size = 256 images
7. Compressor / Dimensionality Reduction
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encoder
compressor
input
feature vector
• Save compressed version
“bottleneck”
coding
latent space
encoder
9. Features for Another Analytic
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encoder
compressor
another
analytic
input
feature vector
• Autoencoder features are input to
another analytic
• Classification analytic
• Image analytic
• Whatever
Latent space,
code
encoder
g(x)
other features
10. Anomaly Detector
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encoder
compressor
decoder
generator
input
feature vector
same input
feature vector
• If reconstruction loss is too big,
then it can’t be represented by a
coding ==> anomaly
“bottleneck”
coding
latent space
encoder decoder
11. Autoencoder
• Autoencoder has to
• Compress input to codings,
• Reconstruct the output given ONLY the codings
• Small reconstruction loss ==> input space successfully compressed to
just the codings
• Expect decrease coding => increased reconstruction loss
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12. IoT Analytics for Eldercare
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13. Impact & business opportunity of a global demographic shift
• US – Estimated assets for this
demographic $8.4 to $11.6 Trillion
• China – Estimated “silver hair” market
to rise to $17 Trillion by 2050,
amounting to a third of the Chinese
economy.
• Japan – Estimated 65+ financial
assets $9.1 trillion
• Rising Eldercare costs will disrupt
economies 6% of US GDP and 4 to
8% of EU GDP will account for social
service costs for the Elder. PercentageofPopulation65yearsandolder
Japan
Italy
Germany
Ireland
China
Australia
Brazil
US
India
Egypt
2017
•http://www.icis.com/blogs/chemicals-and-the-economy/2015/03/worlds-demographic-dividend-turns-deficit-populations-age/
•https://www.metlife.com/assets/cao/mmi/publications/studies/2010/mmi-inheritance-wealth-transfer-baby-boomers.pdf
•http://blogs.ft.com/ftdata/2014/02/13/guest-post-adapting-to-the-aging-baby-boomers/
•http://www.marketsandmarkets.com/Market-Reports/healthcare-data-analytics-market-905.html
•http://www.bloomberg.com/bw/articles/2014-09-25/chinas-rapidly-aging-population-drives-652-billion-silver-hair-market
•Asian Journal of Gerontology & Geriatrics for Centenarians: According to the National Institute of Population and Social Security Research, Japan had 67,000 centenarians in 2014, but that number is forecast to reach 110,000 in 2020, 253,000 in 2030 and peak at 703,000 in the year 2051.
14. ADLs (Activities of Daily Living)
• Activities we normally do. Determines level of care needed.
• Bathing and showering
• Personal hygiene and grooming (including brushing/combing/styling hair)
• Dressing
• Toileting (getting to the toilet, cleaning oneself, and getting back up)
• Eating (self-feeding not including cooking or chewing and swallowing)
• Functional mobility, often referred to as "transferring", as measured by the ability to
walk, get in and out of bed, and get into and out of a chair; the broader definition
(moving from one place to another while performing activities) is useful for people
with different physical abilities who are still able to get around independently.
• We expect to see additional ADLs in our data
• Sleeping, Watching TV, …
14 https://en.wikipedia.org/wiki/Activities_of_daily_living
15. Avamere – High Density Sensor Deployment
Instrumenting 20 Patient rooms in Skilled Nursing Facility
& 5 Independent Living Apartment
Over 1000 sensors deployed
16. Autoencoder
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encoder
compressor
decoder
generator
input
feature vector
same input
feature vector
• output = 3 x 30 features
“bottleneck”
coding
latent space
f(x) “f -1”(x)
Input
• 30 sensors
• 1-minute windows
• sensor fire counts
• 3 adjacent time windows
• 3 x 30 features
23. Conclusions
• Tuning
• Time window: 1 minute is good (5 min was too long)
• Alpha (# concurrent ADLs)
• Ideal: small alpha (0.1, 0.01, …)
• But Spark LDA ML doesn’t allow alpha < 1.0
• Iterations: 100 is good (35 was too few)
• Choose #ADLs up front. 6?, 7?, 10? …
• No ADL looks like “dressing” or “grooming”
• Found non-standard “Watch TV” ADL
• Interpretation
• Must manually characterize sensor sets (ADLs)
• How to transfer learning across apartments (diff sensors) ?
• Encouraging results, but more work is needed
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24. One Neuron in a Neural Network
• Neuron (perceptron) computes weighted sum of inputs,
then activation function
𝑎𝑗 = 𝜎 Σ 𝑘 𝑤 𝑘𝑗 𝑎 𝑘
• Activation function
• Differentiable (nearly everywhere)
• Sigmoid: 𝜎 𝑥 =
exp(𝑥)
1+exp(𝑥)
• soft-max 𝑥 𝑘 =
exp(𝑥 𝑘)
Σ 𝑗 exp(𝑥 𝑗)
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