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) …
"I really do think [nbdev] is a huge step forward for programming environments": Chris Lattner, inventor of Swift, LLVM, and Swift Playgrounds. It is a Python programming environment called nbdev, which allows you to create complete python packages, including tests and a rich documentation system, all in Jupyter Notebooks. We've already written a large programming library (fastai v2) using nbdev, as well as a range of smaller projects. Nbdev is a system for something that we call exploratory programming. Exploratory programming is based on the observation that most of us spend most of our time as coders exploring and experimenting.
BERT is one of the most popular algorithms in the NLP spectrum known for producing state-of-the-art results in a variety of language modeling tasks. Built on top of transformers and seq-to-sequence models, the Bidirectional Encoder Representations from Transformers is a very powerful NLP model that has outperformed many. The state-of-the-art results that it produces on a variety of language-specific tasks are enough to show that it is indeed a big deal. The results come from its underlying architecture which uses breakthrough techniques such as seq2seq (sequence-to-sequence) models and transformers. The seq2seq model is a network that converts a given sequence of words into a different sequence and is capable of relating the words that seem more important.
These days it seems that nearly every product and startup boasts some kind of A.I. capability, but when it comes to advancing this domain beyond simplistic machine learning technologists at MIT Technology Review's Future Compute conference say these A.I. will need to be more human than not. When discussing A.I. during the conference's first day on December 2nd, speakers focused on two distinct paths for this technology: more human-like A.I.'s as well as more computer-like humans. This dual approach was presented as a potential future for human-machine symbiosis. But what exactly does that all mean, and is it even a good thing? A research Scientist from Oak Ridge National Laboratory, Catherine Schuman began the conversation by presenting her work on neuromorphic computing.
This article will take you through how these companies can automate several procedures like menu digitization or invoice processing that are traditionally done manually to save time and operational costs. We have all had moments when we suddenly crave a good dessert. Getting that big tub of ice-cream after a long day at work would've been an inconvenience a few years ago. But food delivery apps can get it to you at a lightning fast speed. With companies like DoorDash, DeliveryHero, GrubHub, FoodPanda, Swiggy, Zomato and Uber Eats competing for a greater market share in the food delivery market, adopting technology that aids companies to scale up their operations has become a necessity to stay relevant.
Computer architecture is currently undergoing a radical and exciting transition as the end of Moore's Law nears, and the burden of increasing humanity's ability to compute falls to the creativity of computer architects and their ability to fuse together the application and the silicon. A case in point is the recent explosion of deep neural networks, which occurred as a result of a drop in the cost of compute because of successful parallelization with GPGPUs (general-purpose graphics processing units) and the ability of cloud companies to gather massive amounts of data to feed the algorithms. As improvements in general-purpose architecture slow to a standstill, we must specialize the architecture for the application in order to overcome fundamental energy efficiency limits that prevent humanity's progress. This drive to specialize will bring another wave of chips with neural-network specific accelerators currently in development worldwide, but also a host of other kinds of accelerators, each specialized for a particular planet-scale purpose. Organizations like Google, Microsoft, and Amazon are increasingly finding reasons to bypass the confines imposed by traditional silicon companies by rolling their own silicon that is tailored to their own datacenter needs.
At the start of the decade, deep learning restored the reputation of artificial intelligence (AI) following years stuck in a technological winter. Within a few years of becoming computationally feasible, systems trained on thousands of labeled examples began to exceed the performance of humans on specific tasks. One was able to decode road signs that had been rendered almost completely unreadable by the bleaching action of the sun, for example. It just as quickly became apparent, however, that the same systems could just as easily be misled. In 2013, Christian Szegedy and colleagues working at Google Brain found subtle pixel-level changes, imperceptible to a human, that extended across the image would lead to a bright yellow U.S. school bus being classified by a deep neural network (DNN) as an ostrich.
On a warm day in April 2013, I was sitting in a friend's kitchen in Paris, trying to engineer serendipity. I was trying to get my computer to write music on its own. I wanted to be able to turn it on and have it spit out not just any goofy little algorithmic tune but beautiful, compelling, mysterious music; something I'd be proud to have written myself. The kitchen window was open, and as I listened to the sounds of children playing in the courtyard below, I thought about how the melodies of their voices made serendipitous counterpoint with the songs of nearby birds and the intermittent drone of traffic on the rue d'Alésia. In response to these daydreams, I was making a few tweaks to my software--a chaotic, seat-of-the-pants affair that betrayed my intuitive, self-taught approach to programming--when I saw that Bill Seaman had just uploaded a new batch of audio files to our shared Dropbox folder. I had been collaborating with Bill, a media artist, on various aspects of computational creativity over the past few years.
One of the leading forms of cancer is colorectal cancer (CRC), which is responsible for increasing mortality in young people. The aim of this paper is to provide an experimental modification of deep learning of Xception with Swish and assess the possibility of developing a preliminary colorectal polyp screening system by training the proposed model with a colorectal topogram dataset in two and three classes. The results indicate that the proposed model can enhance the original convolutional neural network model with evaluation classification performance by achieving accuracy of up to 98.99% for classifying into two classes and 91.48% for three classes. For testing of the model with another external image, the proposed method can also improve the prediction compared to the traditional method, with 99.63% accuracy for true prediction of two classes and 80.95% accuracy for true prediction of three classes.
Today, we're happy to announce that the Deep Graph Library, an open source library built for easy implementation of graph neural networks, is now available on Amazon SageMaker. In recent years, Deep learning has taken the world by storm thanks to its uncanny ability to extract elaborate patterns from complex data, such as free-form text, images, or videos. However, lots of datasets don't fit these categories and are better expressed with graphs. Intuitively, we can feel that traditional neural network architectures like convolution neural networks or recurrent neural networks are not a good fit for such datasets, and a new approach is required. A Primer On Graph Neural Networks Graph neural networks (GNN) are one of the most exciting developments in machine learning today, and these reference papers will get you started.
Organizations are increasingly looking to adopt blockchain technologies for alternative data storage. And with heaps of data distributed across blockchain ledgers, the need for data analytics with AI is growing. The combination of AI and blockchain is fueling the onset of the "Fourth Industrial Revolution" by reinventing economics and information exchange. From healthcare to government, the potent combination of both AI and blockchain is slowly but surely transforming industries. Google DeepMind is developing an "auditing system for healthcare data".