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As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc
The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.
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This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.
Powerful Google developer tools for immediate impact! (2023-24 C)
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Imagine a world where information flows as swiftly as thought itself, making decision-making as fluid as the data driving it. Every moment is critical, and the right tools can significantly boost your organization’s performance. The power of real-time data automation through FME can turn this vision into reality. Aimed at professionals eager to leverage real-time data for enhanced decision-making and efficiency, this webinar will cover the essentials of real-time data and its significance. We’ll explore: FME’s role in real-time event processing, from data intake and analysis to transformation and reporting An overview of leveraging streams vs. automations FME’s impact across various industries highlighted by real-life case studies Live demonstrations on setting up FME workflows for real-time data Practical advice on getting started, best practices, and tips for effective implementation Join us to enhance your skills in real-time data automation with FME, and take your operational capabilities to the next level.
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What are drone anti-jamming systems? The drone anti-jamming systems and anti-spoof technology protect against interference, jamming, and spoofing of the UAVs. To protect their security, countries are beginning to research drone anti-jamming systems, also known as drone strike weapons. The anti-jam and anti-spoof technology protects against interference, jamming and spoofing. A drone strike weapon is a drone attack weapon that can attack and destroy enemy drones. So what is so unique about this amazing system?
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Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.
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TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
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Concurrency & Ruby
1.
CONCURRENCY & RUBY Rocky Jaiswal RubyConf India 2013
2.
WHY CONCURRENCY?
3.
ABOUT ME Learning programming for the last 11 years Did Java for around 8 years Started learning Ruby ~3 years back ♥ Ruby ♥ the Ruby community Also learning some CoffeeScript and Scala http://rockyj.in @whatsuprocky
4.
CONCURRENCY? Concurrency is when two tasks can start, run, and complete in overlapping time periods Concurrency can be implemented even in single processing units to speed things up Concurrency is non-deterministic Whereas a parallel program is one that merely runs on multiple processors, with the goal of hopefully running faster than it would on a single CPU
5.
THREADS VS PROCESSESS Threads are light weight processes that run in the same memory context Ruby has Green Threads which are managed by the Ruby process JRuby has real OS thread that run parallel to the parent thread
6.
THREADS IN RUBY
7.
SAMPLE UNICORN SETUP 15 Unicorns = 15 Processes 1 Unicorn Process ~= 150 MB 15 Processes ~= 2 GB RAM* Scaling this means more processes = more memory = more money Also, If you are CPU bound you want to use no more unicorn processes than you have cores, otherwise you overload the system and slow down the scheduler.
8.
CONCURRENCY IS GOOD JRuby + Puma / Torquebox High-Scalability with less memory Resque / Sidekiq More workers and faster processing with less memory
9.
SO IS IT ALL DOOM AND GLOOM? No! Most Rails applications are IO bound With MRI you are always thread safe MRI is getting faster and GC is getting better Processes management is optimized Passenger is using a hybrid - evented + threaded / process architecture
10.
THREAD-SAFETY LET ME GIVE YOU A DEMO Appending to Arrays: MRI Version vs JRuby Version DEMO
11.
RUN CODE ON MRI & JRUBY array = [] 5.times.map do Thread.new do #Init 5 threads 1000.times do array << nil #In each thread add 1000 elements to the Ar end end end.each(&:join) puts array.size
12.
EVEN APPENDING TO ARRAYS IS NOT THREAD SAFE!
13.
WHAT ABOUT RAILS config.threadsafe! def threadsafe! @preload_frameworks = true @cache_classes = true @dependency_loading = false @allow_concurrency = true self end
14.
JRUBY ON RAILS DEMO
15.
BAD COUNTER CODE class PagesController < ApplicationController @counter = 0 class << self attr_accessor :counter end #Classic read-modify-write problem def index counter = self.class.counter # read sleep(0.1) counter += 1 #update sleep(0.1) self.class.counter = counter # write users = User.all puts "-----------" + self.class.counter.to_s + "------------" end end
16.
UGLY SYNCHRONIZED CODE class PagesController < ApplicationController @counter = 0 @semaphore = Mutex.new class << self attr_accessor :counter attr_accessor :semaphore end def index #counter = self.class.counter # read sleep(0.1) self.class.semaphore.synchronize { self.class.counter += 1 #update } sleep(0.1) #self.class.counter = counter # write users = User.all puts "-----------" + self.class.counter.to_s + "------------" end end
17.
RAILS 4 IS CONCURRENCY ENABLED BY DEFAULT
18.
CONCURRENCY INTRODUCES Race Conditions Deadlocks Starvation etc. BUT GIVES YOU Speed Less Memory Usage
19.
SAFE CONCURRENCY Don't do it. If you must do it, don't share data across threads. If you must share data across threads, don't share mutable data. If you must share mutable data across threads, synchronize access to that data.
20.
THREAD SAFETY IN JRUBY LOCKS ATOMICITY IMMUTABILITY
21.
ATOMIC COUNTER java_import 'java.util.concurrent.atomic.AtomicInteger' class PagesController < ApplicationController @counter = AtomicInteger.new(1) class << self attr_accessor :counter end def index sleep(0.1) counter = self.class.counter.getAndIncrement() #update sleep(0.1) users = User.all puts "-----------------" + counter.to_s + "-----------------" end end
22.
ALL THIS SUCKS! 95% of syncronized code is broken. The other 5% is written by Brian Goetz. - Venkat Subramaniam
23.
ENTER ACTOR
24.
THE ACTOR MODEL Introduced by Carl Hewitt in 1973 Contributions by a lot of scholars and universities Popularized by Erlang, now in Scala Simple and high-level abstractions for concurrency and parallelism Objects are Actors each with their own state which is never shared Communication happens through messages Very lightweight event-driven processes (approximately 2.7 million actors per GB RAM [Akka])
25.
THE ACTOR MODEL -2 Easier to deal with humans than with threads Like humans, Actors communicate via messages No state sharing, communicate via immutable messages
26.
IMPLEMENTATIONS
27.
PRODUCER CONSUMER PROBLEM Demo with JRuby + Locks Demo with JRuby + Celluloid
28.
PRODUCER CONSUMER with locks HTTPS://GIST.GITHUB.COM/ROCKY- JAISWAL/5847810
29.
PRODUCER CONSUMER with actors HTTPS://GIST.GITHUB.COM/ROCKY- JAISWAL/5847814
30.
SUMMARY Concurrency is the need of the hour MRI is thread safe by default due to GIL / GVL JRuby gives you real concurrency (RBX as well) With power comes responsibility Don't worry, concurrency can be easy if you follow the ground rules If you want to write concurrent code yourself, use Actors * I did not cover STM (provided by Clojure)
31.
THANK YOU! QUESTIONS #A lot of this content has been taken from blogs, wikis and books. I do not claim it is my own and I wholeheartedly thank everyone who helped me with this presentation.
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