Collaborating Authors

Machine learning, concluded: Did the "no-code" tools beat manual analysis?


I am not a data scientist. And while I know my way around a Jupyter notebook and have written a good amount of Python code, I do not profess to be anything close to a machine learning expert. So when I performed the first part of our no-code/low-code machine learning experiment and got better than a 90 percent accuracy rate on a model, I suspected I had done something wrong. If you haven't been following along thus far, here's a quick review before I direct you back to the first two articles in this series. To see how much machine learning tools for the rest of us had advanced--and to redeem myself for the unwinnable task I had been assigned with machine learning last year--I took a well-worn heart attack data set from an archive at the University of California-Irvine and tried to outperform data science students' results using the "easy button" of Amazon Web Services' low-code and no-code tools.

Embedded Systems Engineer (Robotics)


We are a next-gen cybernetics start-up backed by a few top-tier investors (led by NEA). We aim to push the boundaries of what intelligent systems are capable of achieving both autonomously and in collaboration with humans. Before starting Neo Cybernetica, our CEO founded the unicorn AI company DataRobot and led for almost a decade while working directly with worldwide customers across many industries. You can expect to be part of something exciting at the contour of human knowledge. We are looking for an Embedded Systems Engineer to join our fast-growing team of highly skilled professionals and work on breakthrough robotics technology.

Deepfakes expose vulnerabilities in certain facial recognition technology


Mobile devices use facial recognition technology to help users quickly and securely unlock their phones, make a financial transaction or access medical records. But facial recognition technologies that employ a specific user-detection method are highly vulnerable to deepfake-based attacks that could lead to significant security concerns for users and applications, according to new research involving the Penn State College of Information Sciences and Technology. The researchers found that most application programming interfaces that use facial liveness verification--a feature of facial recognition technology that uses computer vision to confirm the presence of a live user--don't always detect digitally altered photos or videos of individuals made to look like a live version of someone else, also known as deepfakes. Applications that do use these detection measures are also significantly less effective at identifying deepfakes than what the app provider has claimed. "In recent years we have observed significant development of facial authentication and verification technologies, which have been deployed in many security-critical applications," said Ting Wang, associate professor of information sciences and technology and one principal investigator on the project.

Brain implants could let lawyers scan years of material in a fraction of the time, report suggests

Daily Mail - Science & tech

Electronic brain implants could allow lawyers to quickly scan years of background material and cut costs in the future, a new report claims. The report from The Law Society sets out the way the profession could change for employees and clients as a result of advances in neurotechnology. It suggests that a lawyer with the chip implanted in his or her brain could potentially scan documentation in a fraction of the time, reducing the need for large teams of legal researchers. 'Some lawyers might try to gain an advantage over competitors and try to stay ahead of increasingly capable AI systems by using neurotechnology to improve their workplace performance,' wrote Dr Allan McCay, the author of the report. Neurotechnology could also allow firms to charge clients for legal services based on'billable units of attention' rather than billable hours, as they would be able to monitor their employees' concentration.

Real-World Machine Learning--PyTorch and Monai for Healthcare Imaging


To improve your skills in machine learning and artificial intelligence, it is important to solve real-world problems. What better problem to solve then helping to save people's lives? Machine learning is being used more and more in the field of healthcare. PyTorch and Monai can be used to discover tumors in livers. We just published a course on the

Remodelling a learning problem


What is another way to re-cast this? In the case of Image segmentation, we can think of it as an optimization problem. For any image, you can choose and iterate between a variety of segment configurations, and choose the one where the difference between pixel intensities is the highest. Learning problems are trained by minimizing the error between predictions and actual values across a large data set. In other words, they are trained by optimization methods that are stochastic. Learning and optimization problems have many things in common structurally.

3 questions to separate AI from marketing hype


One of the myriad challenges of being a modern technology leader is separating the marketing hype from reality when it comes time to procure new hardware or software. Product marketing often tends toward hyperbole and focuses on the positive rather than the negative. With technology products, there's the added wrinkle of complex technical elements that require specialized understanding. Mix the historical hyperventilation of most product marketing with a hot technology, and you're forced to wallow through a dense wall of promises, buzzwords and claims to determine if a product will work for your organization. This is especially true in the era of artificial intelligence, where it seems everything from supply chain software to office furniture claims to have some element of AI embedded.



In this course, I will explain in a practical and intuitive way how PyTorch works. We will go beyond the use of the API which will allow you to continue your journey in machine learning and/or differentiable programming with more confidence. In the first part, we will implement (in Python, from scratch) our own differentiable programming framework, which will be very similar to PyTorch. This will allow you to understand how PyTorch, TensorFlow, JAX, etc. work. Then, we will focus on PyTorch and see the basic tensor operations, the calculation of gradients and the use of graphics cards (GPUs).

AI will reshape health care--we will determine if it's for better or worse

MIT Technology Review

Thank you for joining us on "The cloud hub: From cloud chaos to clarity." Health care organizations have many emerging opportunities to apply artificial intelligence in new ways. To do it right, AI in health care should always begin with a patient-centered approach.

The path to successful conversational AI capabilities

MIT Technology Review

Thank you for joining us on "The cloud hub: From cloud chaos to clarity." Conversational AI technologies are entering an era of hyper-personalized, multimodal assistants that are empathetic, inclusive, and immersive. Enterprises should take a gradual approach to conversational AI, increasingly moving toward complex features with continuous incremental advancements.