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Why Effective and Ethical AI Needs Human-Centered Design
Data science is about a half-century old in the way we think of it now, but with the advent of AI, we’re reaching a precipice of how we want to model our AI initiatives. Moving forward with not just effective but ethical AI, we need a human-centered design principle.... Read more
Exploring the Deep Learning Framework PyTorch
There are a variety of open-source deep learning frameworks to choose from including Keras, TensorFlow, Caffe2, and MXNet among others. At ODSC West in 2018, Stephanie Kim, a developer at Algorithmia, gave a great talk introducing the deep learning framework PyTorch. Primarily developed by Facebook, PyTorch enables a suite... Read more
Watch: Beyond the Hype. Real Companies Doing Real Business with AI
AI – everyone is talking about it but who is actually doing it (and generating business results). This session takes an industry by industry perspective on true AI adoption disambiguating the hype from the reality, the theoretical from the practical and the research labs from ROI—real companies doing real... Read more
Watch: The Future of Machine Learning
See the video from Accelerate AI West 2019 where keynote, Alex Holub, talks about where the biggest innovations in applied Machine Learning will occur in the next 5 years. He is discussing how some of the largest global organizations are using Machine Learning today, and the near future of... Read more
ML Operationalization: From What and Why? to How and Who?
Operationalization may be the newest 18 letter word in AI, but there are specific steps to removing your AI initiative from the silos and putting it into production at scale. Sivan Metzger of ParallelM is here to share his experiences, mistakes and all, deploying machine learning and building a... Read more
The Past, Present, and Future of Automated Machine Learning
As a consultant in data science and machine learning, and also a tech journalist, I’m in a position to recognize current trends in the industry. One of the latest crazes centers around “automated machine learning” or AutoML as many call it. In fact, I’ve written a couple of articles... Read more
A Manager’s Guide to Starting a Computer Vision Program
So you’re thinking of starting a computer vision program, but you’ve realized now that the logistics are overwhelming. What framework do you use? What infrastructure? Do you go with an out of the box solution or take the time to build your own? Cloud GPU or on-premise? What’s your... Read more
How To Democratize Artificial Intelligence in Your Business
Olivier Blais of Moov.ai  has had a lot of experience building AI initiatives for organizations. He’s been a lot of places and is well aware of the hype and hysteria surrounding AI. He’s here to help you and your company build better AI initiatives, democratize Artificial Intelligence, and alleviate some... Read more
Best Practices for Deploying Machine Learning in the Enterprise
If you’re an organization worried about being left behind with deploying machine learning, it’s not just you. According to Gartner’s Hype Cycle Chart, machine (and deep) learning are the biggest hyped trends of the year. More businesses, organizations, and startups are talking about deep learning and what it means... Read more
Deep Learning for Speech Recognition
Deep learning is well known for its applicability in image recognition, but another key use of the technology is in speech recognition employed to say Amazon’s Alexa or texting with voice recognition. The advantage of deep learning for speech recognition stems from the flexibility and predicting power of deep... Read more