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) …
One can intuitively surmise artificial intelligence (AI) has gained significant traction in businesses, academia and government in recent years. Now, there is data that documents growth across many indicators, including startups, venture capital, job openings and academic programs. These bellwethers were captured in the AI Index, produced under the auspices of was conceived within Stanford University's Human-Centered AI Institute and the One Hundred Year Study on AI (AI100). One key measure of AI development is startups and venture capital funding. From January 2015 to January 2018, active AI startups increased 2.1x, while all active startups increased 1.3x, the report states.
This year, we've had the chance to chat in depth high up the data scientist totem pole on how to operationalize AI projects. We've found that data science is the fundamental building block. We've also found that, while data and insight are the fuel, collaboration and agility are lubricants that grease the skids. Ultimately, that places a premium on the people and process sides of AI projects. The spotlight might be on the skills, the access to powerful GPUs, and the frameworks for developing the algorithms.
In the early hours of Aug. 25, 2017, a ragged insurgent group from Myanmar's Rohingya Muslim minority attacked military outposts in the country's northwest, killing 12 people. Security forces quickly retaliated with a campaign of village burning and mass killings that lasted weeks. As Rohingya died by the thousands, Myanmar's military leaders took to Facebook. A post from the commander-in-chief pledged to solve "the Bengali problem," using a pejorative for Rohingya in Myanmar. Another general wrote to praise the "brilliant effort to restore regional peace," observing that "race cannot be swallowed by the ground but only by another race."
In an interdisciplinary project funded by a Canadian Institute for Advanced Research (CIFAR) Catalyst grant, researchers at the University of Guelph and the University of Toronto, Mississauga combined expertise in fruit fly biology with machine learning to build a biologically-based algorithm that churns through low-resolution videos of fruit flies in order to test whether it is physically possible for a system with such constraints to accomplish such a difficult task. Fruit flies have small compound eyes that take in a limited amount of visual information, an estimated 29 units squared. The traditional view has been that once the image is processed by a fruit fly, it is only able to distinguish very broad features. But a recent discovery that fruit flies can boost their effective resolution with subtle biological tricks has led researchers to believe that vision could contribute significantly to the social lives of flies. This, combined with the discovery that the structure of their visual system looks a lot like a Deep Convolutional Network (DCN), led the team to ask: "can we model a fly brain that can identify individuals?"
Machine learning researcher Irina Nicolae is here to dispel a common misconception: You don't have to be a math whiz to end up working in technology. Growing up in Bucharest, Romania, Irina had relatively little interest in numerics. She was, however, captivated by machinery and how different parts fit together to perform a task. It was this fascination that eventually led her to programming. Today, Irina is turning her longtime passion into action in her role as a research scientist at IBM Research – Ireland.
Catchy Christmas songs can now be created by a special songwriting AI, taught by studying existing festive tunes. The system came up with catchy jingles with names like'Syllabub Chocolatebell', 'Peaches Twinkleleaves' and'Cocoa Jollyfluff'. Researchers from Made by AI trained a neural network by inputting one hundred Christmas tunes in the form of Musical Instrument Digital Interface (MIDI) files. It then picked out recurring themes, motifs, instruments and rhythms to generate its own hits. Scientists have trained an AI system to write its own catchy Christmas songs by teaching it existing festive tunes.
We've heard from customers that scaling TensorFlow training jobs to multiple nodes and GPUs successfully is hard. TensorFlow has distributed training built-in, but it can be difficult to use. Recently, we made optimizations to TensorFlow and Horovod to help AWS customers scale TensorFlow training jobs to multiple nodes and GPUs. With these improvements, any AWS customer can use an AWS Deep Learning AMI to train ResNet-50 on ImageNet in just under 15 minutes. To achieve this, 32 Amazon EC2 instances, each with 8 GPUs, a total 256 GPUs, were harnessed with TensorFlow. All of the required software and tools for this solution ship with the latest Deep Learning AMIs (DLAMIs), so you can try it out yourself. You can train faster, implement your models faster, and get results faster than ever before. This blog post describes our results and shows you how to try out this easier and faster way to run distributed training with TensorFlow. Figure A. ResNet-50 ImageNet model training with the latest optimized TensorFlow with Horovod on a Deep Learning AMI takes 15 minutes on 256 GPUs.
One of the most common uses of neural networks is the generation of new content, given certain constraints. A neural network is created, then trained on source content – ideally with as much reference material as possible. Then, the model is asked to generate original content in the same vein. This generally has mixed, but occasionally amusing, results. The team at [Made by AI] had a go at generating Christmas songs using this very technique.
Enrollment in artificial intelligence (AI) introductory courses in the United States grew by 3.4 times between 2012 and 2017, and introductory machine learning (ML) classes grew by five times during that same period. That's according to the latest AI Index 2018 Report, a rich collection of data intended to serve as a "comprehensive resource" for anybody interested in the field. The information was contributed by universities, companies, consultancies and associations. The report observed that ML courses are on a faster trajectory for growth than AI at this point. While the University of California Berkeley's introductory AI course grew by a little under two times between 2012 and 2017, its ML course had 6.8 times as many students.
The objective of a neural network is to have a final model that performs well both on the data that we used to train it (e.g. the training dataset) and the new data on which the model will be used to make predictions. The central challenge in machine learning is that we must perform well on new, previously unseen inputs -- not just those on which our model was trained. The ability to perform well on previously unobserved inputs is called generalization.