We look at science as something very elite, which only a few people can learn. That's just not true. You just have to start early and give kids a foundation. Kids live up, or down, to expectations.” - Mae Jemison
Welcome to a new episode of our special edition podcast series called “Makers Today!”, where we highlight the movers and shakers in the Maker space. In this month’s episode, my guest is teacher, maker, and author Lorraine Underwood. Lorraine and I chat about her participation in the the #badass Women Makers and Engineers Contest at element14, the cool projects she has created, and why she wrote the book Save the World with Code.
FloatingPoint DSP for You and Me: Why Today’s Real World Designs Need FloatingPoint Digital Signal Processing
Taking Piracy out of the Driver’s Seat
Crafting the Next Generation of AI - Deci is Looking to Change How We Build Artificial Intelligence
Fish Fry Special Edition: Makers Today! Max Maxfield
Building a Bridge Between Technology and Diplomacy: The Purdue Research Foundation Launches the Center for Tech Diplomacy at Purdue
Papa’s Got a Brand New Node: Intel Makes Waves in Process and Packaging
Automatic For The People: CacheQ’s New Compiler for Multithreaded Executables
Machine Learning and The New Age of Intelligent Chip Design
The Hidden Security Risks of Automotive Electronic Systems
Better than a Human Driver: Trusted Electronics and Functional Safety
Machine Vision Meets Hackathon: ADLINK’s 20/20 Hack Solves Real World Problems
Quantum Practicality and You: How Intel is Solving the Quantum Computing Interconnect Bottleneck
How LoRa Can Help Us Build a Smarter and Safer Planet
Fish Fry Special Edition: Makers Today! Karen Corbeill
Let's Get Speedy! Digital Signal Processing for Always-On Systems, Sensor Fusion and Computer Vision
The Digital Thread: The Advancement of Digital Backbones and the World’s First Digital Fiber Fabric
Connecting the Disconnected: Avoiding Design Pitfalls with Altium’s Nexar Network
Computations in Memory: Imec’s Analog Inference Accelerator Brings Extreme Energy Efficiency to Neural Network Acceleration
The Problem is Exponential: The Value of Pre-silicon verification and Pre-silicon Hardware Debug
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