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AI's next big leap


A few years ago, scientists learned something remarkable about mallard ducklings. If one of the first things the ducklings see after birth is two objects that are similar, the ducklings will later follow new pairs of objects that are similar, too. Hatchlings shown two red spheres at birth will later show a preference for two spheres of the same color, even if they are blue, over two spheres that are each a different color. Somehow, the ducklings pick up and imprint on the idea of similarity, in this case the color of the objects. What the ducklings do so effortlessly turns out to be very hard for artificial intelligence. This is especially true of a branch of AI known as deep learning or deep neural networks, the technology powering the AI that defeated the world's Go champion Lee Sedol in 2016. Such deep nets can struggle to figure out simple abstract relations between objects and reason about them unless they study tens or even hundreds of thousands of examples.

5 Ways to Deal with the Lack of Data in Machine Learning - KDnuggets


In many projects I carried out, companies, despite having fantastic AI business ideas, display a tendency to slowly become frustrated when they realize that they do not have enough data… However, solutions do exist! The purpose of this article is to briefly introduce you to some of them (the ones that are proven effective in my practice) rather than to list all existing solutions. The problem of data scarcity is very important since data are at the core of any AI project. The size of a dataset is often responsible for poor performances in ML projects. Most of the time, data related issues are the main reason why great AI projects cannot be accomplished.

When AI sees a man, it thinks "official." A woman? "Smile"


Turns out, computers do too. When US and European researchers fed pictures of members of Congress to Google's cloud image recognition service, the service applied three times as many annotations related to physical appearance to photos of women as it did to men. The top labels applied to men were "official" and "businessperson"; for women they were "smile" and "chin." The researchers administered their machine vision test to Google's artificial intelligence image service and those of rivals Amazon and Microsoft. Crowdworkers were paid to review the annotations those services applied to official photos of lawmakers and images those lawmakers tweeted.

Adapting on the fly to test time distribution shift


Imagine that you are building the next generation machine learning model for handwriting transcription. Based on previous iterations of your product, you have identified a key challenge for this rollout: after deployment, new end users often have different and unseen handwriting styles, leading to distribution shift. One solution for this challenge is to learn an adaptive model that can specialize and adjust to each user's handwriting style over time. This solution seems promising, but it must be balanced against concerns about ease of use: requiring users to provide feedback to the model may be cumbersome and hinder adoption. Is it possible instead to learn a model that can adapt to new users without labels?

Facebook & Its Tumultuous Relationship With AI-Based Content Moderation


During a press meet recently, a Facebook spokesperson said that the social media giant would be redoubling its efforts to counter'harmful content' on its platform using artificial intelligence. Reportedly, Ryan Barnes, the Facebook Product Manager of Community Integrity, said that the company would use AI to prioritise harmful content. This move is targeting at helping its over 15,000 human reviewers and moderators in dealing with reported contents. Barnes said during the press interaction, "We want to make sure we're getting to the worst of the worst, prioritising real-world imminent harm above all." With that being said, there have been numerous attempts in the past to bring AI into the content moderation process on Facebook's platforms. However, not all of them have met with success.

The way we train AI is fundamentally flawed – MIT Technology Review


It's no secret that machine-learning models tuned and tweaked to near-perfect performance in the lab often fail in real settings. This is typically put down to a mismatch between the data the AI was trained and tested on and the data it encounters in the world, a problem known as data shift. For example, an AI trained to spot signs of disease in high-quality medical images will struggle with blurry or cropped images captured by a cheap camera in a busy clinic. Now a group of 40 researchers across seven different teams at Google have identified another major cause for the common failure of machine-learning models. Called "underspecification," it could be an even bigger problem than data shift.

What enterprise CISOs need to know about AI and cybersecurity


Modern day enterprise security is like guarding a fortress that is being attacked on all fronts, from digital infrastructure to applications to network endpoints. That complexity is why AI technologies such as deep learning and machine learning have emerged as game-changing defensive weapons in the enterprise's arsenal over the past three years. There is no other technology that can keep up. It has the ability to rapidly analyze billions of data points, and glean patterns to help a company act intelligently and instantaneously to neutralize many potential threats. Beginning about five years ago, investors started pumping hundreds of millions of dollars into a wave of new security startups that leverage AI, including CrowdStrike, Darktrace, Vectra AI, and Vade Secure, among others.

Google's Chimera Painter can create fantastical creatures automatically


Google has created a new machine learning model that can automatically create renderings from a user supplied creature outline. The trained machine learning model is called Chimera Painter. It's a machine learning model that can act as a paintbrush for a digital artist to reduce the amount of time required for creating high-quality art without impacting artistic choice. Chimera Painter is a demo application that can add features and textures to a creature outline segmented with body parts labeled with labels like "wings" or "claws." When the user presses the transform button, the algorithm would create the artwork for them.



This repo contains archival material about "F# for AI Models". FM was a prototype F# eDSL for writing numeric models. It has now been subsumed by DiffSharp 1.0. This is now being merged to DiffSharp 1.0. Models written in FM can be passed to optimization and training algorithms utilising automatic differentiation without any change to modelling code, and can be executed on GPUs and TPUs using TensorFlow.

What is the right Data Annotation Process for Training the Machine Learning Algorithms?


Data annotation in AI world is one of the most crucial processes to make available the set of training data for machine learning algorithms. And computer vision based AI model needs annotated images to make the various objects recognizable for better understanding of surroundings. Data annotation process involves from collection of data to labeling, quality check and validation that makes the raw data usable for machine learning training. For supervised machine learning projects, without labeled data, it is not possible to train the AI model. During the whole process, well trained human power with right tools and techniques, data is annotated as per the requirements and then processed in a highly secured environment to clients.