TensorFlow & Other Frameworks

Tuesday, October 25, 2022

- PDT
PRO Workshop (AI): How Route Optimisation Can Be Scaled and Optimised Using Meta Heuristics for Realistic Scenario
Sushant Burnawal
Sushant Burnawal
Publicis Sapient, Senior Associate Level 1

ECommerce platforms drive the current era, and the COVID pandemic gave rise to the need for home delivery. The end consumers have multiple options to cater for their needs, and in that case, the eCommerce platforms have to provide on-time and quality delivery to stay ahead in the market and, at the same time, boost their profit margins.

Route Optimization is one of the most critical aspects of planning and transportation. It ensures that deliveries always arrive on time and carry out with the lowest possible cost and energy consumption. However, there are a lot of variables that eCommerce platforms need to consider in a real-time scenario.

During this unfortunate COVID pandemic, eCommerce platforms deal with a massive inflow of e-commerce orders from customers scattered throughout a city, country or even across the globe. This gives rise to an enormous number of variables come into play that cannot be solved using conventional methods in a reasonable amount of time. With the recent developments in AI, machine learning and cloud data, the entire game of route optimization has begun to change. AI continuously retrieves data, learns from it, and searches for improved methods to ensure the most optimal routes for the drivers.

In the novel solution, we are trying to solve the multi-objective vehicle routing problem with optimization variables like minimizing the delivery cost, the number of vehicles and delivery time. To show this as a real-life simulation, we will dissect through the open-source library of veroviz combined with innovative scaling solutions to showcase the real-time implementation of route optimization in any part of the world. 

- PDT
PRO Workshop (AI): Sparsity without Sacrifice – How to Accelerate AI Models Without Losing Accuracy
Anshuman Mishra
Anshuman Mishra
Numenta, Principal Researcher
Lawrence Spracklen
Lawrence Spracklen
Numenta, Director of Machine Learning Architecture

Most companies with AI models in production today are grappling with stringent latency requirements and escalating energy costs. One way to reduce these burdens is by pruning such models to create sparse lightweight networks. Pruning involves the iterative removal of weights from a pre-trained dense network to obtain a network with fewer parameters, trading off against model accuracy. Determining which weights should be removed in order to minimize the impact to the network’s accuracy is critical. For real-world networks with millions of parameters, however, analytical determination is often computationally infeasible; heuristic techniques are a compelling alternative.In this presentation, we talk about how to implement commonly-used heuristics such as gradual magnitude pruning (GMP) in production, along with their associated accuracy-speed trade offs, using the BERT family of language models as an example.Next, we cover ways of accelerating such lightweight networks to achieve peak computational efficiencies and reduce energy consumption. We walk through how our acceleration algorithms optimize hardware efficiency, unlocking order-of-magnitude speedups and energy savings.Finally, we present best practices on how these techniques can be combined to achieve multiplicative effects in reducing energy consumption costs and runtime latencies without sacrificing model accuracy.


- PDT
PRO Workshop (AI): Scaling ML Embedding Models to Serve a Billion Queries
Senthilkumar Gopal
Senthilkumar Gopal
eBay, Senior Engineering Manager, Search ML

This talk is aimed at providing a deeper insight into the scale, challenges and solutions formulated for powering embeddings based visual search in eBay. This talk walks the audience through the model architecture, application archite for serving the users, the workflow pipelines produced for building the embeddings to be used by Cassini, eBay's search engine and the unique challenges faced during this journey. This talk provides key insights specific to embedding handling and how to scale systems to provide real time clustering based solutions for users. 

- PDT
PRO Workshop (AI): Artificial General Intelligence with GPT-3 with Open AI
Cameron Vetter
Cameron Vetter
Octavian Technology Group, Principal Architect

Large Language Models (LLM) have come out of the realm of academia and research and become available to average development teams thanks to the efforts of Open AI and their competitors. Now that we have access to them what can we do with them?

This talk will explore some of the practical uses for GPT-3 made available through Open AI. We will start with a brief introduction to LLM's and transformers and how they bring us a step closer to artificial general intelligence. We will focus on real demonstrations. Each capability will start with a canned demonstration and move on to ad hoc input provided by the audience.

• Text Generation
○ Turn complex text into a simple summary
○ Create an outline of an essay
• Conversation
○ Sarcastic chat bot
• Code Generation
○ Explain Python Code
○ Translate text into programmatic commands
• Question Answering
○ Factual Answering

You will leave this talk with an understanding of Large Language Models and their practical use cases. Walk away inspired on how to apply large language models to your business today! 

Wednesday, October 26, 2022

- PDT
OPEN TALK (AI): Deep Dive on Creating a Photorealistic Talking Avatar
Sebastiano Galazzo
Sebastiano Galazzo
Synapsia.ai, Artificial intelligence researcher

Creating a photorealistic avatar speaking any sentence starting from a written input text.

Focusing on autoencoders, we will do a journey from the beginning (Of the speaker experience), mistakes and tips learned along the path.
Will be showcased:

- Intro, the timeline from beginning to nowadays
- Is NOT a deepfake
- Audio processing techniques: STFT (Short Term Fourier Transform), MELs and custom solutions
- Deeplearning models and architecture
- The technique, inspired to inpaiting, used to animate the mouth
- Masks and convolution
- Landmarks extraction
- Morphing animation technique based on autoencoders features
- Microsoft Azure Speech services used to support audio and animation processing
- Putting all together 

- PDT
OPEN TALK (AI): Pushing Deepfakes to the Limit - Fake Video Calls with AI
Martin Förtsch
Martin Förtsch
TNG Technology Consulting GmbH, Principal Consultant
Thomas Endres
Thomas Endres
TNG Technology Consulting GmbH, Partner
Jonas Mayer
Jonas Mayer
TNG Technology Consulting GmbH, Senior Consultant

Today's real-time Deepfake technology makes it possible to create indistinguishable doppelgängers of a person and let them participate in video calls. Since 2019, the TNG Innovation Hacking Team has intensively researched and continuously developed the AI around real-time Deepfakes. The final result and the individual steps towards photorealism will be presented in this talk.

Since its first appearance in 2017, Deepfakes have evolved enormously from an AI gimmick to a powerful tool. Meanwhile different media outlets such as "Leschs Kosmos", Galileo and other television formats have been using TNG Deepfakes.

In this talk we will show the different evolutionary steps of the Deepfake technology, starting with the first Deepfakes and ending with real-time Deepfakes of the entire head in high resolution. Several live demos will shed light on individual components of the software. In particular, we focus on various new technologies to improve Deepfake generation, such as Tensorflow 2 and MediaPipe, and the differences in comparison to our previous implementations. 

Tuesday, November 1, 2022

- PDT
[#VIRTUAL] PRO Workshop (AI): How Route Optimisation Can Be Scaled and Optimised Using Meta Heuristics for Realistic Scenario
Sushant Burnawal
Sushant Burnawal
Publicis Sapient, Senior Associate Level 1

ECommerce platforms drive the current era, and the COVID pandemic gave rise to the need for home delivery. The end consumers have multiple options to cater for their needs, and in that case, the eCommerce platforms have to provide on-time and quality delivery to stay ahead in the market and, at the same time, boost their profit margins.

Route Optimization is one of the most critical aspects of planning and transportation. It ensures that deliveries always arrive on time and carry out with the lowest possible cost and energy consumption. However, there are a lot of variables that eCommerce platforms need to consider in a real-time scenario.

During this unfortunate COVID pandemic, eCommerce platforms deal with a massive inflow of e-commerce orders from customers scattered throughout a city, country or even across the globe. This gives rise to an enormous number of variables come into play that cannot be solved using conventional methods in a reasonable amount of time. With the recent developments in AI, machine learning and cloud data, the entire game of route optimization has begun to change. AI continuously retrieves data, learns from it, and searches for improved methods to ensure the most optimal routes for the drivers.

In the novel solution, we are trying to solve the multi-objective vehicle routing problem with optimization variables like minimizing the delivery cost, the number of vehicles and delivery time. To show this as a real-life simulation, we will dissect through the open-source library of veroviz combined with innovative scaling solutions to showcase the real-time implementation of route optimization in any part of the world. 

- PDT
[#VIRTUAL] PRO Workshop (AI): Sparsity without Sacrifice – How to Accelerate AI Models Without Losing Accuracy
Anshuman Mishra
Anshuman Mishra
Numenta, Principal Researcher
Lawrence Spracklen
Lawrence Spracklen
Numenta, Director of Machine Learning Architecture

Most companies with AI models in production today are grappling with stringent latency requirements and escalating energy costs. One way to reduce these burdens is by pruning such models to create sparse lightweight networks. Pruning involves the iterative removal of weights from a pre-trained dense network to obtain a network with fewer parameters, trading off against model accuracy. Determining which weights should be removed in order to minimize the impact to the network’s accuracy is critical. For real-world networks with millions of parameters, however, analytical determination is often computationally infeasible; heuristic techniques are a compelling alternative.In this presentation, we talk about how to implement commonly-used heuristics such as gradual magnitude pruning (GMP) in production, along with their associated accuracy-speed trade offs, using the BERT family of language models as an example.Next, we cover ways of accelerating such lightweight networks to achieve peak computational efficiencies and reduce energy consumption. We walk through how our acceleration algorithms optimize hardware efficiency, unlocking order-of-magnitude speedups and energy savings.Finally, we present best practices on how these techniques can be combined to achieve multiplicative effects in reducing energy consumption costs and runtime latencies without sacrificing model accuracy.


- PDT
[#VIRTUAL] PRO Workshop (AI): Scaling ML Embedding Models to Serve a Billion Queries
Senthilkumar Gopal
Senthilkumar Gopal
eBay, Senior Engineering Manager, Search ML

This talk is aimed at providing a deeper insight into the scale, challenges and solutions formulated for powering embeddings based visual search in eBay. This talk walks the audience through the model architecture, application archite for serving the users, the workflow pipelines produced for building the embeddings to be used by Cassini, eBay's search engine and the unique challenges faced during this journey. This talk provides key insights specific to embedding handling and how to scale systems to provide real time clustering based solutions for users. 

- PDT
[#VIRTUAL] PRO Workshop (AI): Artificial General Intelligence with GPT-3 with Open AI
Cameron Vetter
Cameron Vetter
Octavian Technology Group, Principal Architect

Large Language Models (LLM) have come out of the realm of academia and research and become available to average development teams thanks to the efforts of Open AI and their competitors. Now that we have access to them what can we do with them?

This talk will explore some of the practical uses for GPT-3 made available through Open AI. We will start with a brief introduction to LLM's and transformers and how they bring us a step closer to artificial general intelligence. We will focus on real demonstrations. Each capability will start with a canned demonstration and move on to ad hoc input provided by the audience.

• Text Generation
○ Turn complex text into a simple summary
○ Create an outline of an essay
• Conversation
○ Sarcastic chat bot
• Code Generation
○ Explain Python Code
○ Translate text into programmatic commands
• Question Answering
○ Factual Answering

You will leave this talk with an understanding of Large Language Models and their practical use cases. Walk away inspired on how to apply large language models to your business today! 

- PDT
[#VIRTUAL] PRO Workshop (AI): Intro to Machine Learning with ML.NET
David Patrick
David Patrick
DSA, MCT, MCSD, MCSE, MVP

Come and get immersed into the world of machine learning with this introduction and demonstration of to ML.NET. We'll show how to create an app that can predict the type of iris flower based on features such as petal length. We'll show how to download and install ML.NET, create a data set, write the required c# code and run the finished app. 

Wednesday, November 2, 2022

- PDT
[#VIRTUAL] OPEN TALK (AI): Pushing Deepfakes to the Limit - Fake Video Calls with AI
Thomas Endres
Thomas Endres
TNG Technology Consulting GmbH, Partner
Martin Förtsch
Martin Förtsch
TNG Technology Consulting GmbH, Principal Consultant
Jonas Mayer
Jonas Mayer
TNG Technology Consulting GmbH, Senior Consultant

Today's real-time Deepfake technology makes it possible to create indistinguishable doppelgängers of a person and let them participate in video calls. Since 2019, the TNG Innovation Hacking Team has intensively researched and continuously developed the AI around real-time Deepfakes. The final result and the individual steps towards photorealism will be presented in this talk.

Since its first appearance in 2017, Deepfakes have evolved enormously from an AI gimmick to a powerful tool. Meanwhile different media outlets such as "Leschs Kosmos", Galileo and other television formats have been using TNG Deepfakes.

In this talk we will show the different evolutionary steps of the Deepfake technology, starting with the first Deepfakes and ending with real-time Deepfakes of the entire head in high resolution. Several live demos will shed light on individual components of the software. In particular, we focus on various new technologies to improve Deepfake generation, such as Tensorflow 2 and MediaPipe, and the differences in comparison to our previous implementations. 

Thursday, November 3, 2022

- PDT
[#VIRTUAL] OPEN TALK (AI): Deep Dive on Creating a Photorealistic Talking Avatar
Sebastiano Galazzo
Sebastiano Galazzo
Synapsia.ai, Artificial intelligence researcher

Creating a photorealistic avatar speaking any sentence starting from a written input text.

Focusing on autoencoders, we will do a journey from the beginning (Of the speaker experience), mistakes and tips learned along the path.
Will be showcased:

- Intro, the timeline from beginning to nowadays
- Is NOT a deepfake
- Audio processing techniques: STFT (Short Term Fourier Transform), MELs and custom solutions
- Deeplearning models and architecture
- The technique, inspired to inpaiting, used to animate the mouth
- Masks and convolution
- Landmarks extraction
- Morphing animation technique based on autoencoders features
- Microsoft Azure Speech services used to support audio and animation processing
- Putting all together 

- PDT
[#VIRTUAL] PRO TALK (AI): Using Machine Learning to detect fraud in Payments
Tamsin Crossland
Tamsin Crossland
Icon Solutions, Senior Architect

This presentation discusses how Machine Learning can be applied to Payments to respond rapidly to known and emerging patterns of fraud, and to detect patterns of fraud that may not otherwise be identified.
It will cover techniques that have been used and are emerging in fraud detection including rule-based techniques, supervised learning and unsupervised learning. The presentation includes a demonstration using TensorFlow to detect fraud. This will illustrate the process of preparing training and test data, learning and then applying the model to generate potential fraud events.
The talk will also explore potential issues including data bias and mitigating approaches.