Everyone in this thread seems to be focused on testing, testing, testing. My best advice is to read (and understand) a bunch of articles. Get a good mental image of the landscape of your field, and figure out what you want to work on from there. Read increasingly specific articles, and get increasingly specific about what you want to research. At the same time, keep an open mind and don't focus too hard on your specific topic: you want to get ideas from elsewhere as well to boost your creativity.

I published a multipart "Tutorial on Graph Neural Networks for Computer Vision and Beyond" starting from some basics [1], then an overview explaining several important methods [2] and a separate post on spectral convolution [3]. I know there are a lot of blog posts on graph networks already, but in my tutorial I tried to explain key (and sometimes complicated) ideas in very simple terms from a computer vision perspective, so it should be good for those with a computer vision and machine learning background. I provide detailed Python and PyTorch examples to clarify differences between methods. Otherwise, feel free to downvote or remove. Any questions or feedback is very welcome, especially, if you notice some mistakes or confusing info.

Abstract: Artificial neural networks (ANNs) have now been widely used for industry applications and also played more important roles in fundamental researches. Although most ANN hardware systems are electronically based, optical implementation is particularly attractive because of its intrinsic parallelism and low energy consumption. Here, we propose and demonstrate fully-functioned all optical neural networks (AONNs), in which linear operations are programmed by spatial light modulators and Fourier lenses, and optical nonlinear activation functions are realized with electromagnetically induced transparency in laser-cooled atoms. Moreover, all the errors from different optical neurons here are independent, thus the AONN could scale up to a larger system size with final error still maintaining in a similar level of a single neuron. We confirm its capability and feasibility in machine learning by successfully classifying the order and disorder phases of a typical statistic Ising model.

Look at imitation learning literature. If you have oracle access to the expert, that's good. If you only have a fixed dataset of state-action pairs (like learning from a recorded video of a human), it's a lot harder because your policy will inevitably make mistakes and visit parts of the state space where the expert never visited, so there is no training data to follow.

In February, an artificial intelligence lab cofounded by Elon Musk informed the world that its latest breakthrough was too risky to release to the public. OpenAI claimed it had made language software so fluent at generating text that it might be adapted to crank out fake news or spam. On Thursday, two recent master's graduates in computer science released what they say is a re-creation of OpenAI's withheld software onto the internet for anyone to download and use. Aaron Gokaslan, 23, and Vanya Cohen, 24, say they aren't out to cause havoc and don't believe such software poses much risk to society yet. The pair say their release was intended to show that you don't have to be an elite lab rich in dollars and PhDs to create this kind of software: They used an estimated $50,000 worth of free cloud computing from Google, which hands out credits to academic institutions.

We all know that artificial intelligence is playing a huge role in how businesses operate. AI programs and services are helping transform everything from data collection and processing in the marketing department to on-boarding in the HR department. While AI and automation hold tremendous value in terms of time and cost savings internally, there is another area in which AI promises even bigger, more meaningful returns: customer experience. As I've said many times before, customer experience is the root of digital transformation. Every tech decision we make should return to this core foundation.

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When Andrew Kaplan reminisces, his engrossing tales leave the impression that he's managed to pack multiple lives into a single existence: globe-trotting war correspondent in his 20s, a member of the Israeli army who fought in the Six-Day War, successful entrepreneur and, later, the author of numerous spy novels and Hollywood scripts. Now -- as the silver-haired 78-year-old unwinds with his wife of 39 years in a suburban oasis outside Palm Springs -- he has realized he would like his loved ones to have access to those stories, even when he's no longer alive to share them. Kaplan has agreed to become "AndyBot," a virtual person who will be immortalized in the cloud for hundreds, perhaps thousands, of years. If all goes according to plan, future generations will be able to interact with him using mobile devices or voice computing platforms such as Amazon's Alexa, asking him questions, eliciting stories and drawing upon a lifetime's worth of advice long after his physical body is gone. Someday, Kaplan -- who playfully refers to himself as a "guinea pig" -- may be remembered as one of the world's first "digital humans."

Artificial intelligence (AI) promises impelling potential for the growth of society and economy in today's times.

NEORIS, a tech consultancy that accelerates the digital capabilities of enterprises, today announced their global partnership with Neo4j, the leader in graph databases, to deliver unparalleled insights and capabilities to their clients around the world. The NEORIS Augmented Intelligence Platform now integrates Neo4j's leading graph database technology into its Knowledge Fabric Layer, enabling more intuitive data analysis and machine learning capabilities which are augmented by connected data for deeper insights, The platform serves as the key strategic enabler of NEORIS' recently launched Augmented Intelligence Practice by accelerating an enterprise's time to actionable insights. The Augmented Intelligence Platform is composed of four logical layers, namely Information Harvesting, Knowledge Fabric, Enterprise AI, and Human-centered Interfaces, for a complete framework that is poised to accelerate the deployment of next-generation, graph-powered AI digital solutions. "Artificial intelligence continues its push into areas such as smart supply-chains, advanced fraud management, risk management, regulatory compliance and delivering hyper-personalized products and services. The convergence of AI, graph technology and analytics provides an immense opportunity to develop learning applications grounded in the context of connected data, and thus capable of situational awareness. Aggregating large amounts of data across organizational silos, establishing intelligent relationships and connecting this knowledge with advanced AI models are redefining businesses," said Anthony DeLima, Global CTO and Head of NEORIS U.S. "We are excited about this partnership for many reasons, but most notably because it will enable us to work with our clients to deliver an entirely new generation of solutions that augment human thinking and decision-making."