AI At The Edge Hack Chat ((NEW))
To help us along the road to incorporating spatial AI into our projects, Erik Kokalj will stop by the Hack Chat. Erik does technical documentation and support at Luxonis, a company working on the edge of spatial AI and computer vision. Join us as we explore the depths of spatial AI.
On the other hand, bad actors can also take advantage of AI in several ways. "For instance, AI can be used to identify patterns in computer systems that reveal weaknesses in software or security programs, thus allowing hackers to exploit those newly discovered weaknesses," Finch said.
Cybersecurity program might have access to "vast resources from Silicon Valley and the like [to] build some very good defenses against low-grade AI cyber attacks," Finch said. "When we get into AI developed by hacker nation states [such as Russia and China], their AI hack systems are likely to be quite sophisticated, and so the defenders will generally be playing catch up to AI-powered attacks."
The structure of the event was to hack for a week, demo for the public for a few hours, and crown a winner. One winner would be judged by human judges and the other by a GPT-3 bot based on the feedback it aggregated from everyone in attendance.
ChatGPT is a prototype chatbot released by OpenAI. The chatbot is powered by AI and is gaining more traction than previous chatbots because it not only interacts in a conversational manner but has the capability to create code and many other complex questions and requests.
Developments in cutting-edge technology often steal headlines for a short period of time then fade away into academic or commercial use, but AI is something that is now becoming a part of everyday life. We, often unknowingly, interact with AI through chat bots, online shopping, fraud prevention, and voice assistant. The amount of security research in this area is growing but adoption of best practices outlined in that research could be lagging the latest technological developments.
While ChatGPT is obviously a useful tool to educate, it can also be a useful tool in developing attacks. Once an attacker has found a vulnerability, ChatGPT can be used to help develop and correct exploits. Put simply, the chatbot can be used as a virtual colleague to help discuss and perfect exploits. This can also be demonstrated by asking if code snippets are secure. In this simple SQL Injection example, a PHP code snippet was provided to ChatGPT. Once ChatGPT has identified a weakness in a code snippet, it can be asked it to create a cURL request for exploitation:
Jayatu and Abhishek will introduce American Express as a global organization, and provide insight on its journey to become an analytics-focused company. They will walk through a set of use cases showcasing how billions of data inputs, combined with the right AI/ML techniques and business insights, deliver a significant competitive edge for success.
NLP's latest pre-trained language models like BERT, GPT2, TransformerXL, XLM, etc. are achieving state of the art results in a wide range of NLP tasks. In this hack session, community favourite and Kaggle Grandmaster SRK will compare the performance of these different pre-trained models along with pre-trained word vector models on classification tasks.
Deploying a model poses several challenges, such as model versioning, containerization of the model, etc. Web frameworks like Flask and Django can be used to wrap the model into a REST API and expose the API. But this solution requires developers to write and maintain code to handle requests to the model and support other deployment-related features as well. Tata in this hack session will do a deep dive into Tensorflow Serving which is a flexible, high-performance serving system for machine learning models, designed for production environments with a use case.
Although machine learning, by its very nature, is always a form of statistical discrimination, the discrimination becomes objectionable when it places certain privileged groups at a systematic advantage and certain unprivileged groups at a systematic disadvantage. In this hack session, Rajesh and Prateek from IBM will discuss the concepts and capabilities of a model to test for biases and explanations. along with the wider spectrum of explainability methods, notably data explanations, metrics and persona specific explanations.
As a data engineer, Spark & Kafka are essential tools to work with streaming data and build robust pipelines for the same. Durgaraju in this hack session will give an overview of streaming analytics and then demonstrate the integration of Kafka and Spark Structured Streaming
The recent proliferation of digital games for commercial, social, and educational purposes has necessitated a new research direction towards game intelligence and knowledge discovery. The key quest is to enable end-to-end informatics around game dynamics, game platforms, and the players using the immense volume of multi-dimensional data. This talk will cover the fundamental building blocks in the domain of game intelligence and informatics.
Graphs naturally represent data created in a host of real-world processes, including interactions between people on social or communication networks, relations between entities in a knowledge graph, links between content with its creators and consumers in content platforms, and many others. Sourabh who has won multiple competitions by creating effective graph-based features would do a deep dive on how to build graph-based features and further use them to build high-performance ML models.
This exciting hack session by Xander covers one of the greatest ideas in Deep Learning of the past couple of years: Generative Adversarial Networks. He will first explain how a generative adversarial network (GAN) really works. He will then dive into Nvidia's StyleGAN model and learn how one can manipulate it's latent space to morph arbitrary images of faces.
In this hack session, we will cover the motivations behind developing a robust pipeline for handling handwritten text. Learn about an interesting use case where Deep Learning (DL) techniques are being utilized to generate synthetic data for training along with some interesting architectures for the same.
This hack session presents an introduction to deep-learning based question-answer models. These models by virtue of the underlying transfer learning layer (using contextualized word embeddings such as BERT) can easily find exact answers to factoid questions from a corpus of documents on which they were not trained.
This hack session will walk participants through deploying machine learning models locally and on a cloud platform. An overview of relevant principles from software engineering and DataOps disciplines will be covered with focus on doing all of these at scale.
Combined with more traditional content-based recommendation systems, image-based recommendations can help to increase robustness and performance, for example, by better matching a particular customer style. In this hack session, learn how to build content-based recommender systems using image data.
Improving search results offers huge payoffs for retailers. Machine learning can improve ecommerce search results every time a customer shops on the website, taking into account personal preferences and purchase history. Instead of using traditional search methods like keyword matching, machine learning can generate a search ranking based on relevance for that particular user. Atul and Sonu in this hack session would cover code walkthrough of deep learning search systems along with the deployment aspects for the same.
Solving multiple time series (more than 100 million time series) in a single shot has always been a challenging task for traditional machine learning models. LSTMs are capable of solving multi-time series problems with a capability to learn embeddings of categorical features for each object (time series). This hack session will involve end-to-end Neural Network architecture walkthrough and code running session in PyTorch which includes data loader creation, efficient batching, Categorical Embeddings, Multilayer Perceptron for static features and LSTM for temporal features.
Chatbots are everywhere today, from booking your flight tickets to ordering food, chances are that you have already interacted with one. But do you think making an intelligent chatbot is hard? Do you want to be able to quickly build a reasonably good chatbot for your business? In this workshop, you will build multiple intelligent Chatbots using concepts of Machine Learning, Natural Language Processing and the Open Source RASA framework. You will also learn how to deploy them to various chat applications for example - the Slack messenger!
Have you struggled to improve your model beyond a particular score in a data science competition? Do you wonder what goes inside the minds of top data scientists? Then this workshop is what you are looking for. This is a highly interactive experience with Kaggle Grandmaster Pavel Pleskov & Ankit Choudhary who leads the hackathon category at Analytics Vidhya. The goal of this workshop is to provide a forum for exchanging ideas and new approaches to become masters of data science challenges
Because the AI training in swarm learning is done at the edge, using the compute available on the clients, the back and forth to a central control is removed. Blockchain is used in its place; it tracks the interactions in any swarm and makes the swarm more secure.
Finally, in a similar way that edge computing and swarm-based AI are distributed, so is blockchain. Blockchain is simply a distributed ledger, each of whose pages produces a distinct number, or hash. Any change to the input changes the hash. So it provides a forensic record of contents and changes that is both transparent and difficult to hack, or as Christian Reichenbach, worldwide digital adviser for HPE Pointnext, puts it, a fingerprint.
The process allows a user to sell or exchange information without giving away the underlying data. Monetizing information processed at the edge is one of the promises that technologies like swarm learning offer. 2b1af7f3a8