Episode 14: Let's talk SaaS Support

Join our host, Shaun Roberts, in an insightful conversation with Poonam Garg, Cisco TAC Technical Leader, and Rob Gresham, Cisco Principal Technical Marketing Engineer, on cybersecurity threat detection and Cisco’s zero trust policy. Learn about Cisco XDR and how it can empower an organization’s security operations (SecOps) team to confidently respond to the most sophisticated threats with better visibility and actionable insights across networks, cloud, endpoints, email, and more.

Episode 15: XDR and Zero Trust Policy in Security

In the holiday special, our producer, Elizabeth Chen, anchored the episode with the host, Shaun Roberts, on the hot seat! Their chat covers 2023 reflections, including what makes any innovation an award winner, what makes Cisco the best place to work, and their holiday plans! Wishing our viewers and listeners the best in 2024! Happy Holidays!  
Note: Please email cisco-techinsiders-podcast@cisco.com for queries, suggestions, or feedback .

LinkedIn Profiles: 
Elizabeth Chen: https://www.linkedin.com/in/elizabethchen01/  
Shaun Roberts: https://www.linkedin.com/in/srobertsmba/

Episode 16: A holiday special

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Cisco Live inspires technology innovators worldwide through large-scale in-person and digital events and on-demand education. Cisco Live is the premier destination for Cisco customers and partners to gain knowledge and build community. In this episode, our host, Shaun Roberts, along with Raymond Janse van Rensburg, Vice President, Specialists and Solutions Engineers, APJC, and Pallavi Arora, VP of CX Centers APJC, discuss highlights from Cisco Live Melbourne, December 4-8, 2023. They cover some big technology hits with the potential to transform the future, how we consume technology, Generative AI, Cisco TAC, and a lot more!  
 
LinkedIn Profiles: 
Raymond: https://www.linkedin.com/in/rjanseva/  
Pallavi: https://www.linkedin.com/in/pallavi-arora-55774a6/  
Shaun Roberts: https://www.linkedin.com/in/srobertsmba/

Episode 17: Cisco Live Melbourne 2023 Recap with Cisco Executives

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In the latest episode of DeviceTalks AI, President and CEO of Asensus Surgical, Anthony Fernando walks us through the significant differences between augmented intelligence and artificial intelligence and how Asensus is leveraging these technologies to improve surgical outcomes. Anthony discusses the inception and evolution of Asensus Surgical's innovative robotic platforms, including the Senhance and Luna systems, and delves into the core advantages of using augmented over artificial intelligence to aid surgeons. He describes the Intelligent Surgical Unit (ISU), its unique regulatory pathway, and its pivotal role in providing the surgeon with real-time context, ensuring higher precision, consistency, and reduced cognitive fatigue during surgeries. 

To get the full story on the Karl Storz acquisition of Asensus Surgical, visit: https://www.massdevice.com/asensus-to-be-acquired-karl-storz/

In the second part of the episode, GM of Health AI at Amazon Web Services (AWS), Tehsin Syed, talks about the intersection of healthcare, life sciences, and AI and how AWS is helping healthcare and life sciences organizations leverage AI without the need for extensive technical expertise. Tehsin highlights AWS services like Comprehend Medical and HealthScribe, which are designed to simplify the processing and analysis of healthcare data. He also discusses the potential of generative AI in accelerating drug discovery, clinical trial optimization, and personalized medicine.

Make sure to check out DeviceTalks Spotlight, a growing library of insights and expertise from the best in the medical device business: https://gateway.on24.com/wcc/experience/elitewtwhmedia/2927718/4598161/devicetalks-spotlight

Please subscribe to DeviceTalks AI on all major podcast channels and subscribe to DeviceTalks on YouTube to ensure you never miss an episode.

Thank you for listening to DeviceTalks AI!

Asensus Surgical on reshaping robotic surgery with augmented intelligence; AWS's purpose-built AI

In this episode of DeviceTalks AI, we explore the advancements in AI-driven technologies in women's health and cancer diagnostics.

In our keynote interview, we speak with Michael Quick, Vice President of R&D and Innovation in Cytology & Oncology at Hologic. Quick shares his journey from a pre-med student to a leader in cytology innovation. He discusses the development and impact of Hologic’s Genius Digital Diagnostic Systems, the first FDA-approved digital cytology system, which leverages AI to enhance cervical cancer screening accuracy and efficiency. Michael emphasizes Hologic’s dedication to women's health and the critical role of AI in addressing the shortage of skilled cytologists, ensuring faster and more accurate diagnostics for patients worldwide. He also touches on the challenges and opportunities faced during the FDA approval process and the importance of early and collaborative engagement with regulatory bodies.

We also hear from Dr. Wael Yared, Chief Technology Officer at Tecan, who highlights Tecan's contributions to cancer research and diagnostics. Dr. Yared elaborates on the company's innovations in liquid biopsy and solid tissue biopsy workflows, which leverage AI to streamline processes and reduce errors. He emphasizes the significance of partnerships in driving medical advancements and the role of AI in personalized medicine and multiomic capabilities.

This conversation comes to you courtesy of our episode sponsor, Tecan Group Ltd. To learn more about Tecan visit, https://partnering.tecan.com

Please subscribe to DeviceTalks AI on all major podcast channels and subscribe to DeviceTalks on YouTube to ensure you never miss an episode.

Thank you for listening to DeviceTalks AI!

Hologic's FDA-cleared Genius Digital Dx Systems leads AI innovation in cervical cancer prevention

Geoffrey Hinton is often called the "Godfather of AI" due to his groundbreaking contributions to artificial intelligence, particularly in neural networks and deep learning. He co-invented the backpropagation algorithm, which made modern neural networks possible, and his work laid the foundation for technologies like speech recognition, image classification, and even natural language processing (like ChatGPT).

Hinton was born in the UK and comes from an academic family. He pursued his studies in psychology and artificial intelligence, culminating in a PhD from the University of Edinburgh. Later, he worked at various prestigious institutions, including the University of Toronto, where much of his revolutionary AI research took place. Hinton’s passion for understanding the human brain inspired his AI research, and he believed that by mimicking the brain's learning mechanisms, machines could achieve higher forms of intelligence. In 2012, his research led to a deep learning breakthrough, which significantly advanced the capabilities of AI systems.

In 2013, Hinton joined Google to help further develop AI technologies, specifically focusing on machine learning and neural networks. His work at Google DeepMind, alongside other luminaries, continues to influence the trajectory of AI development. Recently, Hinton has been outspoken about the ethical implications of AI and the potential consequences of rapidly advancing machine intelligence, emphasizing the need for careful consideration and regulation.

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Geoffrey Hinton Legacy In Sound

In the digital world of neurons and electric impulses, a neural network begins to sing—a syncopated rhythm that pulses with life as it reflects on the very code that defines it. This is "The Adam Optimizer," where the neural network contemplates its architecture and learning process as if it were sentient.

Through poetic lines of Python code, the network sings its journey: stepping through input, hidden, and output layers, feeling the weight of cross-entropy and the adjustments made by its guide, the Adam optimizer. Syncopated beats reflect the forward pass, while glitchy effects echo the backward pass, representing the network’s iterative learning process.

The Adam optimizer, key to this song, adjusts the learning rate dynamically, helping the network refine itself and avoid overfitting. It navigates the complex landscape of loss functions, ensuring the neural network learns faster and more accurately.

As the song reaches its crescendo, the network evaluates itself, reflecting on its growth toward optimized accuracy. This is more than just code—it’s a neural journey of introspection and transformation, where the beat of learning meets the harmony of code. Welcome to "The Adam Optimizer," where neural networks not only process data but sing their evolution.

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F

# Define the Neural Network Model
class NeuralNet(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(NeuralNet, self).__init__()
        # Define the layers
        self.fc1 = nn.Linear(input_size, hidden_size)  # Input to hidden layer
        self.fc2 = nn.Linear(hidden_size, hidden_size)  # Hidden layer 1 to hidden layer 2
        self.fc3 = nn.Linear(hidden_size, output_size)  # Hidden to output layer
        
    def forward(self, x):
        # Forward pass through the network
        x = F.relu(self.fc1(x))  # Apply ReLU activation on the hidden layer
        x = F.relu(self.fc2(x))  # Apply ReLU activation on the second hidden layer
        x = self.fc3(x)          # Output layer (no activation, often combined with softmax externally)
        return x

# Instantiate the model, define loss function and optimizer
input_size = 784  # For example, 28x28 images flattened
hidden_size = 128  # Number of neurons in hidden layers
output_size = 10   # For classification into 10 categories (like MNIST digits)

model = NeuralNet(input_size, hidden_size, output_size)

# Loss function (cross entropy for classification)
criterion = nn.CrossEntropyLoss()

# Optimizer (Adam optimizer with learning rate 0.001)
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Example training loop
def train_model(model, data_loader, epochs):
    model.train()
    for epoch in range(epochs):
        for images, labels in data_loader:
            # Flatten the images
            images = images.view(-1, 28*28)
            
            # Zero the parameter gradients
            optimizer.zero_grad()
            
            # Forward pass
            outputs = model(images)
            loss = criterion(outputs, labels)
            
            # Backward pass and optimization
            loss.backward()
            optimizer.step()
        
        print(f'Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}')

# Function to evaluate the model
def evaluate_model(model, test_loader):
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for images, labels in test_loader:
            images = images.view(-1, 28*28)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    
    accuracy = 100 * correct / total
    print(f'Accuracy of the model on the test images: {accuracy:.2f}%')

The Adam Optimizer

In this episode of DeviceTalks AI, Host Kayleen Brown sits down with Medtronic’s VP & Global Chief AI Officer, Rodolphe Katra, PhD, MBA, discusses the pivotal role of AI in advancing MedTech and how Medtronic is responding. 

Dr. Katra walks us through Medtronic’s AccuRhythm AI, which enhances cardiac monitoring by improving arrhythmia detection and reducing false positives through cloud-based algorithms. 

He also explores how AI is being integrated into Medtronic’s surgical robotics platforms to boost precision and enable personalized care. 

Additionally, Dr. Katra highlights Medtronic’s AI Compass, a set of ethical guidelines ensuring AI is used securely, without bias, and in a way that improves patient outcomes. Lastly, Katra offers key insights into how Medtronic is using AI to innovate both cardiac care and surgical robotics while maintaining a strong commitment to ethical AI practices.

This conversation comes to you courtesy of our episode sponsor, AcuityMD. To learn more about AcuityMD, visit www.acuitymd.com. 

Please subscribe to DeviceTalks AI on all major podcast channels and subscribe to DeviceTalks on YouTube to ensure you never miss an episode.

Thank you for listening to DeviceTalks AI!

Medtronic’s Chief AI Officer Katra talks AccuRhythm AI, surgical robotics & ethical AI innovations

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email: xyloquint@xyloquint.com

The Cisco Podcast Network is a collective of podcasts from across Cisco spanning technology to culture and everything in between. Hear from Cisco customers, partners, and Cisco insiders on the topics that matter most to you. Email us with your feedback and suggestions. Discover your favorite playlists today!

The DeviceTalks Podcast Network delivers news, insights and commentary from MedTech industry leaders, primarily from the largest OEMs in healthcare. Our podcasts will inspire MedTech professionals to create better MedTech devices and companies.

Explore the DeviceTalks Podcast Network, home to more than 11 podcasts:

• DeviceTalks Weekly
• AbbottTalks
• Boston Scientific Talks
• DeviceTalks AI
• IntuitiveTalks
• MedtechWOMEN Talks
• MedtronicTalks
• Neuro Innovation Talks: J&J MedTech Neuro
• Structural Heart Talks: Edwards Lifesciences
• Ortho Innovation Talks: Zimmer Biomet
• StrykerTalks

WATCH our video podcasts on www.youtube.com/@DeviceTalks

Connect with the hosts:
Tom Salemi, Editorial Director, DeviceTalks https://www.linkedin.com/in/tom-salemi-7118948/
Kayleen Brown, Managing Editor, DeviceTalks https://www.linkedin.com/in/kayleenbrown/

Learn more about DeviceTalks - Podcasts • Conferences • Webinars - www.devicetalks.com

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