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) …
Can AI make us healthier, happier, better? That's the question on everyone's minds these days. If engineers continue to program AI to take away our jobs and our need to utilize our capacity for deductive reasoning and common sense, then the answer is clearly NO. Human beings thrive on being challenged. It develops will, intelligence, adaptability whether we recognize that fact or not.
Marc Maleh is the Global Director of award-winning advertising firm Havas. He has over 13 years of experience in interactive and emerging technology. Prior to his role as at Havas, Maleh served as VP, Managing Director at R/GA, where he helped grow a team of Data Scientists, technologists, and creatives who built data-driven platforms and campaigns for Nike, LA Dodgers, MD Anderson Cancer Research Center, Samsung, and Verizon. Maleh has also managed international design and technology teams in New York and Shanghai for Screampoint, working with clients that included Apple, AIG, World Trade Center Development, Hudson Yards Development and Jamba Juice. Maleh has been a Judge at the Cannes International Festival of Creativity and has spoken at Fast Company Innovation Festival, Venture Beat AI Conference, NYC Media Lab, General Assembly, NYU, Parsons and Montana State University.
An algorithm developed by researchers at Stanford University proved more effective than human radiologists in diagnosing cases of pneumonia. Much research has been shared on the potential of Artificial Intelligence applied to medicine, and in some cases, can reach a level of accuracy that exceeds the performance of professionals. Following this line, Stanford researchers published a document on CheXNet, the convolutional neuronal network, which they developed with the ability to detect pneumonia symptoms. To do this, he uses the traditional method, chest radiographs. It works with 112,120 images of chest X-rays referring to 14 types of diseases.
Any new technology is not successful until it is embraced and used to its potential. Machine learning is no exception to this rule and its success or to say its ability can be gauged by the trends that exist. Machine learning is already a hot technology at the moments and it seems to have a promising future. At present, it seems to be an evolutionary phase where remarkable developments are expected. This brings us to the thought of what the machine learning of future will be like.
Practical machine learning development has advanced at a remarkable pace. This is reflected by not only a rise in actual products based on, or offering, machine learning capabilities but also a rise in new development frameworks and methodologies, most of which are backed by open-source projects. In fact, developers and researchers beginning a new project can be easily overwhelmed by the choice of frameworks offered out there. These new tools vary considerably -- and striking a balance between keeping up with new trends and ensuring project stability and reliability can be hard. The list below describes five of the most popular open-source machine learning frameworks, what they offer, and what use cases they can best be applied to.
Mindfire, a new foundation with the goal of "decoding the mind" to help develop true artificial intelligence (AI) is launching November 17th in Zurich, Switzerland. Futurism spoke with the founder of Starmind and president of the foundation, Pascal Kaufmann to learn more about its goals and the path to reach them. "We cannot achieve True AI until we understand actual intelligence. Intelligence has evolved as a means of nature to successfully guide us through an ever-changing environment. This gave rise to behavior, emotions, and consciousness.
Driverless cars will be on Britain's roads by 2021 as a result of sweeping regulatory reforms that will put the UK in the forefront of a post-Brexit technological revolution, chancellor Philip Hammond will say this week. In his budget on Wednesday Hammond will allow driverless cars to be tested without any human operator inside or outside the car, and without the legal constraints and rules that apply in many other EU nations, and much of the US. The move – welcomed by the UK motor industry – is part of an attempt by Hammond and the Treasury to project a more upbeat message about the prospects for the UK economy after Brexit, and focus on opportunities as well as the risks. Carmakers have warned that they may have to move at least some production abroad if there is no deal to keep Britain inside the EU single market and customs union, at least for a two-year transition period. But Mike Hawes, chief executive of the Society of Motor Manufacturers and Traders, said it was good news that the government was taking a lead by making the UK attractive to those seeking to develop, test and build an entirely new generation of cars.
Two of the top numerical platforms in Python that provide the basis for Deep Learning research and development are Theano and TensorFlow. Both are very powerful libraries, but both can be difficult to use directly for creating deep learning models. In this post, you will discover the Keras Python library that provides a clean and convenient way to create a range of deep learning models on top of Theano or TensorFlow. Introduction to the Python Deep Learning Library Keras Photo by Dennis Jarvis, some rights reserved. Keras is a minimalist Python library for deep learning that can run on top of Theano or TensorFlow.
A number of weeks ago I solicited feedback from my LinkedIn connections regarding what their typical day in the life of a data scientist consisted of. The response was genuinely overwhelming! Sure, no data scientist role is the same, and that's the reason for the inquiry. So many potential data scientists are interested in knowing what it is that those on the other side keep themselves busy with all day, and so I thought that having a few connections provide their insight might be a useful endeavour. What follows is some of the great feedback I received via email and LinkedIn messages from those who were interested in providing a few paragraphs on their daily professional tasks.
Reporting record quarterly revenues and a 60% rise in earnings since last year, NVIDIA's astronomical rise to the top of the tech market is largely thanks to its range of hardware offerings for AI. "Our Volta GPU has been embraced by every major internet and cloud service provider and computer maker," explained founder and CEO Jensen Huang in a public statement. "Industries across the world are accelerating their adoption of AI." Global e-commerce giants Alibaba and Baidu all announced this quarter that they will adopt NVIDIA Volta GPUs in order to accelerate AI across enterprise and consumer applications, joining Amazon, Facebook, Google, and Microsoft. In other words, it's been a great year for AI innovators – and an even better year for NVIDIA. "We estimate that at least 80% of all applications will have an AI component by 2020," says Dave Schubmehl, Research Director for Cognitive / AI Systems with IDC.