If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
This article is a post in a series on bringing continuous integration and deployment (CI/CD) practices to machine learning. Check back to The New Stack for future installments. For the background and context, we strongly recommend you to read the previous article on the rise of ML PaaS followed by the article on the overview of Azure ML service. In this tutorial, we will build and deploy a machine model to predict the salary from the Stackoverflow dataset. By the end of this, you will be able to invoke a RESTful web service to get the predictions.
Using post-production software to place things realistically in scenes is much tougher for computers than for humans. It requires not only determining an appropriate location for said object, but trying to predict the object's appearance at the target location -- its scale, occlusions, pose, shape, and more. Fortunately, artificial intelligence (AI) promises to lend a hand. In a paper accepted at the NeurIPS 2018 conference last week ("Context-Aware Synthesis and Placement of Object Instances"), researchers at Seoul National University, the University of California at Merced, and Google AI describe a system that learns to insert an object into an image in a "semantically coherent" -- that is to say, convincing -- manner. "Inserting objects into an image that conforms to scene semantics is a challenging and interesting task. The task is closely related to many real-world applications, including image synthesis, AR and VR content editing and domain randomization," the researchers wrote.
Epistemic status / motivation: My file / folder naming and management is all over the place and I hate it. I want to standardized my workflow. TLDR: My machine learning workflow, from simple, to sophisticated, including tools and resources I use in each step. Machine Learning is very simple. Just grab some data pick an algorithm and run it.
Just enter code fccstevens into the promotional discount code box at checkout at manning.com. In part one, we learned about PyTorch and its component parts, now let's take a closer look and see what it can do. In this article, we explore some of PyTorch's capabilities by playing with pre-trained networks. Computer vision -- a field that deals with making computers to gain high-level understanding from digital images or videos -- is certainly one of the fields most impacted by the advent of deep learning, for a variety of reasons. The need for classifying or interpreting the content of natural images was there, huge datasets became available and new constructs, such as convolutional layers, came about and started to run quickly on GPUs with unprecedented accuracies.
In an interesting turn of events, IIT Kharagpur, this week announced that they were to launch a new course on artificial intelligence and machine learning, specially designed for working professionals and engineering students. The programme, which will be of six months duration, will commence from March 2019 and will be conducted from IIT-Kgp institute units in Kharagpur, Bengaluru and Kolkata and possibly in Hyderabad as well. PP Chakrabarti, director at IIT Kharagpur, told the media on Thursday, "A rigorous AI programme for professionals is the need of the hour. The programme has been designed by IIT Kharagpur faculty in consultation with industry experts." This course will comprise 16 one-credit modules and one capstone project.
Machine-learning (ML) technology is radically changing how robots work and dramatically extending their capabilities. The latest crop of ML technologies is still in its infancy, but it looks like we're at the end of the beginning with respect to robots. Much more looms on the horizon. ML is just one aspect of improved robotics. Robotics has demanding computational requirements, and that's being helped by improvements in multicore processing power.
KOLKATA: The Indian Institute of Technology, Kharagpur will launch a six-month Artificial Intelligence (AI) course at three centres in the country, a top official of the institute said Thursday. IIT-KGP, Director, Partha Pratim Chakrabarti told a press meet here that the certified programme is aimed at strengthening India's talent pool in Machine Learning and AI. Chakrabarti said the courses, which will begin from March this year will be offered at IIT KGP's Kolkata facility, at IIT KGP's Kharagpur campus and at a rented premise at Bengaluru. He said thousands of new jobs were being created in AI sector every year with AI growing at 10-15 per cent on annual rate and there was need to have more skilled people in the AI sector. "AI is the future which will more invade our lives in the coming days," Chakrabarti said.
For the Chemputer system to accomplish the automated synthesis of target molecules, we developed a program, the Chempiler, to produce specific, low-level instructions for modular hardware of our laboratory-scale synthesis robot. The Chempiler takes information about the physical connectivity and composition of the automated platform, in the form of a graph using the open-source GraphML format, and combines it with a hardware-independent scripting language [chemical assembly (ChASM) language], which provides instructions for the machine operations of the automated platform. The Chempiler software allows the ChASM code for a protocol to be run without editing on any unique hardware platform that has the correct modules for the synthesis. Formalization of a written synthetic scheme by using a chemical descriptive language (XDL) eliminates the ambiguous interpretation of the synthesis procedures. This XDL scheme is then translated into the ChASM file for a particular protocol.
GREENLIGHT, Business coaching firm, launches a new Artificial Intelligence (AI) simulator product to train startup founders how to overcome obstacles to be sustainable and profitable. The product was code-named "Crucible". A team of Artificial Intelligence (AI) tech developers, gaming experts, and serial entrepreneurs brought their domain expertise into a continuous learning platform and designed crucible product. A proprietary Smart Start framework from Greenlight is ued for assessing and scoring managerial competency and further improving capability with targeted action plans and simulating successful outcomes. Crucible was tested with startups from Columbia University and several candidates competing in the IBM Watson AI XPRIZE.