Machine learning is changing how companies are building technology. From powering a new generation of disruptive products to enabling smarter features in well-known applications we all use and love, ML is at the core of the development process. But with every technology shift comes new challenges. Around 90% of machine learning models never make it into production. Efforts get duplicated as models and datasets aren't shared internally, and similar artifacts are built from scratch across teams all the time.

Highlights: Welcome back to our new Machine Learning series. In the previous post, we studied all about Linear Regression, Cost Functions and Gradient Descent. We also built a simple Linear Regression model using Python. In this tutorial post, we will learn how to make our Linear Regression model faster and more powerful. We will start by building a Linear Regression model using multiple features and then, enhance its performance using various techniques. And finally, we'll implement what we learn about Multiple Linear Regression models using a simple code in Python. In our previous post, we studied an example for predicting the price of a house given the size of the house. In that particular example, we worked with the original version of Linear Regression which utilized only a single feature \(x \), the size of the house, in order to predict \(y \), the price of the house.

Cars are complex machines, blending electronics and mechanics that whizz down the highway at speeds of 60 miles per hour or more. As drivers, we don't necessarily want to think about it, but the fact remains that there's a lot that could go wrong. And this is with our hands on the wheel, eyes on the road, and feet on the pedals. Introduce the concept of autonomous vehicles controlled by self-driving car software running AI algorithms fed by a network of sensors plus other data, and everything gets more complicated still. Fortunately, folks are thinking about exactly these kinds of problems.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. To present a novel concept called artificial intelligence–assisted contour editing (AIACE) and demonstrate its feasibility. The conceptual workflow of AIACE is as follows: given an initial contour that requires clinician editing, the clinician indicates where large editing is needed, and a trained deep learning (DL) model uses this input to update the contour.

Four years ago, the $70 billion Alibaba Group, one of the world's biggest artificial intelligence users, teamed up with Mars, the $35 billion global leader in confectioneries, to figure out the types of candy and chocolates that consumers in China prefer. The fresh consumer data that Alibaba continually gathers from the millions of people shopping on its various platforms turned up the counterintuitive finding that many Chinese who buy chocolates also purchase spicy snacks at the same time. Using that data-driven insight, Mars developed a sweet-and-spicy product: a candy bar that contains Szechuan peppercorns, the source of China's spicy "mala" flavor. Even though Mars didn't conduct any other consumer research to reinforce the A.I.-driven insight, Spicy Snickers proved to be a winner on the mainland. Depending on A.I. also saved the company time; instead of the two to three years that it normally takes to launch a product, Mars was able to bring Spicy Snickers to market for the first time in August 2017, less than 12 months after the collaboration with Alibaba started.

Artificial intelligence has produced creepy images of what it thinks humans will look like in the metaverse. Craiyon AI, a popular text-to-image system, created several different pictures of what people might look like if humans all join the metaverse. Each has an augmented reality headset merged with their face. A number of tech companies, including Mark Zuckerberg's Meta, are pouring billions of dollars to create virtual worlds where people will be able to shop, work and be entertained. The images come after a different AI that works in the same way produced bizarre pictures of the'last selfie ever taken' that showed apocalyptic scenes of people in front of nuclear blasts, with rotting flesh and total devastation behind them.

In June, Embracer's resources were further strengthened through a controversial $1 billion investment by Savvy Gaming Group (SGG), an arm of Saudi Arabia's Public Investment Fund which in turn is owned and operated by Crown Prince Mohammed bin Salman. The investment was met with backlash due to Saudi Arabia's history of human rights abuses and the prince's suspected role in the assassination of Saudi journalist Jamal Khashoggi (Khashoggi was also a columnist for The Washington Post). Embracer CEO Lars Wingefors that the financial support from SGG would not influence how Embracer is run in any way, stating that the company is "built on the principles of freedom, inclusion, humanity and openness," in a subsequent press release.

In this course the student will learn the theory of time series analysis and forecasting. Time series analysis is part of artificial intelligence (AI) and is used by many companies to make predictions on sales, temperature, energy consumption, stock prices, etcetera. Time series analysis involves looking at the time series and making judgements based on the look of the time series. The time series may need to be changed in an attempt to analyse it, and these changes could involve resampling or transforming in some fashion. Time series forecasting involves making predictions on the time series.

Learn to build a shallow neural network to perfectly classify the mnist dataset... a digit dataset using TensorFlow 2.0...

Ironically, the impact on jobs – although widely uncertain – is the part that people professionals are probably already well placed to handle. They will be all too familiar with changes to staffing requirements caused by global shocks, new products and opportunities, or the behaviour of competitors. They will therefore find they can deal with the most talked about bit of AI – the robo-apocalypse on jobs – in their stride. There are, however, a host of other, less well-discussed, challenges that business leaders will need to think about in order to harness the potential that artificial intelligence has to make organisations more efficient and more effective. Artificial intelligence (AI) is an umbrella term for a suite of technologies that performs tasks usually associated with human intelligence.