Update: Part II is out now! This blog post series describes a strategy I've developed over the last couple of years to test Machine Learning projects effectively. Given how uncertain ML projects are, this is an incremental strategy that you can adopt as your project matures; it includes test examples to provide a clear idea of how these tests look in practice, and a complete project implemented with Ploomber is available on GitHub. By the end of the post, you'll be able to develop more robust ML pipelines. Testing Machine Learning projects is challenging. Training a model is a long-running task that may take hours to run and has a non-deterministic output, which is the opposite we need to test software: quick and deterministic procedures.

Artificial Intelligence developers have an uncanny knack for reinventing bunk pseudoscience. Whether it's resuscitating phrenology as facial recognition that can supposedly determine someone's personality or claiming to universally detect emotions based on appearance, the AI field has a long history of claiming to do the impossible. The challenge is that building an algorithm to detect someone's emotions ignores cultural differences and other important factors, Microsoft and University of South California Annenberg researcher Kate Crawford argues in The Atlantic. In an adapted segment of her book, "Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence," Crawford lays out the complicated and flawed history of scientists trying to tie emotion to specific facial movements -- and how AI algorithms attempting to do the same are essentially doomed to fail. Scientists have been trying for decades to codify the facial expressions linked to different emotions, Crawford wrote, and yet it's never worked.

The enormous advantages of AI have a prominent impact on video conferencing, webcasting, and live streaming applications. Deemed growth of digital technology and the rapid evolution of digital platforms unfolded never-ending demands to meet end-user perceptions. Satisfying these needs will help the webcasting and live streaming platforms into a fully automated solution with the utmost competitive advantage. But the question for many of the service providers is how to integrate AI to this solution that can scale and automate according to user needs. The live streaming & Webcasting market is developing like never before.

San Francisco: Tesla and SpaceX CEO Elon Musk in an interview with artificial intelligence researcher Lex Fridman shared advice for students, such as reading books, avoiding becoming a leader, and helping. When asked what advice he would give to young people who want to do something big, Musk simply responded by saying "try to be useful".Also Read - Maybe I'm Partly Chinese: Elon Musk Reacts to Viral Video of His Asian Doppelganger Musk mentioned that the young generation should do things that are useful to fellow human beings and to the world. "It's very hard to be useful," Musk stated, urging young people to "contribute more than you consume". He also advised students to read and develop their general knowledge so they know what's going on around the world. Musk also noted that the more you talk to different kinds of people from all over the world, the more your mind will open.

Across the world, every business sector has found its route to accelerated transformation due to the outbreak of COVID-19 and rapid technological adoption, and the financial sector is not an exception. Technological advances combined with customer expectations are altering the way lenders operate. Furthermore, the increasing internet penetration and adoption of smartphone devices are pulling traditional and new-age borrowers towards digital lending solutions. According to a survey – around 40 per cent of borrowers led by millennials are willing to move to online mode in securing loans rather than offline channels. The accelerated push towards the adoption of digital tools makes technology the key enabler of the digital lending market. Contrary to the conventional lending market, digital lending combats major challenges of the market that served as the bottleneck of growth for many enterprises and individuals in India.

Have you ever pondered about how seamlessly we can drive a car, and why is it so difficult to get a computer to drive a car? It's because our minds are highly evolved and complex and embedding this complexity into a computer is challenging. And today we will cover a tiny fraction of our journey towards achieving self-driving cars. The task I will be discussing in this post is called semantic segmentation. Segmentation as the name suggests is the act of dividing something into separate parts.

Machine learning is the science of making computers work without explicit programming. Over the past decade, machine learning has provided us with self-driving cars, visual recognition, effective web search, and the most advanced understanding of the human genome. Machine learning is so pervasive today that you may be able to use it many times a day without knowing it. The white paper gives you an overview of the various ML blend patterns throughout the ML life cycle, including ML model development, data optimization, training, use, and continuous management. The following table summarizes the eight structural patterns of the ML mixture we discuss in the whitepaper.

The progress that modern society has made in all aspects of science and technology has been awing, amazing, and terrifying. Because with technology comes power, comes complex issues that don't seem to have any real solutions. And the one aspect of technology that captures all these fears, that is a staple in western culture, is artificial intelligence. Now, robots that take over the world, and make humans their servants are far, far away. But not so far away, is a world where humans are unable to compete with the efficiency, skill, and low cost of machines and robots that can complete a range of complex tasks.

Niels Bohr, the Nobel laureate in Physics and father of the atomic model, is quoted as saying, "Prediction is very difficult, especially if it's about the future!" I picked Ron Toews, not to criticize, but to use his AI predictions for 2021 (made last year) as an example of how harrowing this game can be. I'll admit, I'm too timid to make predictions, but I do enjoy tracking how well others did in the previous year. Also, Toews is very well informed about the subject matter, and he's not a vendor, whose "predictions" are not always objective. Here's my quick hits on Toews' ten AI predictions for 2021 - and how they fared.

Predicting accurate protein–ligand binding affinities is an important task in drug discovery but remains a challenge even with computationally expensive biophysics-based energy scoring methods and state-of-the-art deep learning approaches. Despite the recent advances in the application of deep convolutional and graph neural network-based approaches, it remains unclear what the relative advantages of each approach are and how they compare with physics-based methodologies that have found more mainstream success in virtual screening pipelines. We present fusion models that combine features and inference from complementary representations to improve binding affinity prediction. This, to our knowledge, is the first comprehensive study that uses a common series of evaluations to directly compare the performance of three-dimensional (3D)-convolutional neural networks (3D-CNNs), spatial graph neural networks (SG-CNNs), and their fusion. We use temporal and structure-based splits to assess performance on novel protein targets.