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) …
Last week, Qualcomm announced the Snapdragon 845, which sends AI tasks to the most suitable cores. There's not a lot of difference between the three company's approaches -- it ultimately boils down to the level of access each company offers to developers, and how much power each setup consumes. Before we get into that though, let's figure out if an AI chip is really all that much different from existing CPUs. A term you'll hear a lot in the industry with reference to AI lately is "heterogeneous computing." It refers to systems that use multiple types of processors, each with specialized functions, to gain performance or save energy.
I have to predict the performance of an application. The inputs will be time series of past performance data of the application, CPU usage data of the server where application is hosted, the Memory usage data, network bandwidth usage etc. I'm trying to build a solution using LSTM which will take these input data and predict the performance of the application for next one week. I'm able to build a solution which takes one input, ie past performance data of the application. I'm currently stumbled at the part where I have to pass these multiple inputs.
Tech's biggest players have fully embraced the AI revolution. Apple, Qualcomm and Huawei have made mobile chipsets that are designed to better tackle machine learning tasks, each with a slightly different approach. Huawei launched its Kirin 970 at IFA this year, calling it the first chipset with a dedicated neural processing unit (NPU). Then, Apple unveiled the A11 Bionic chip, which powers the iPhone 8, 8 Plus and X. The A11 Bionic features a neural engine that the company says is "purpose-built for machine learning," among other things.
Neural Networks play a very important role when modeling unstructured data such as in Language or Image processing. The idea of such networks is to simulate the structure of the brain using nodes and edges with numerical weights processed by activation functions. The output of such networks mostly yield a prediction, such as a classification. This is achieved by optimizing on a given target using some optimisation loss function. In a previous post, we already discussed the importance of customizing this loss function, for the case of gradient boosting trees.
Machine Learning (ML) powers an increasing number of the applications and services that we use daily. For organizations who are beginning to leverage datasets to generate business insights -- the next step after you've developed and trained your model is deploying the model to use in a production scenario. That could mean integration directly within an application or website, or it may mean making the model available as a service. As ML continues to mature the emphasis starts to shift from development towards deployment. You need to transition from developing models to real world production scenarios that are concerned with issues of inference performance, scaling, load balancing, training time, reproducibility and visibility.
In machine learning, a convolutional neural network (CNN, or ConvNet) is a class of neural networks that has successfully been applied to image recognition and analysis. In this project I've approached this class of models trying to apply it to stock market prediction, combining stock prices with sentiment analysis. The implementation of the network has been made using TensorFlow, starting from the online tutorial. In this article, I will describe the following steps: dataset creation, CNN training and evaluation of the model. In this section, it's briefly described the procedure used to build the dataset, the data sources and the sentiment analysis performed.
Nvidia CEO Jensen Huang showed up at a gathering of artificial intelligence researchers in Long Beach, Calif. One was an orchestral piece inspired by music from the Star Wars movies, but composed by an AI program from Belgian startup AIVA that--of course--relies on Nvidia chips. The music went over big with the crowd of AI geeks attending the Neural Information Processing Systems Conference, known as NIPS, including some giants in the field like Nicholas Pinto, head of deep learning at Apple, and Yann LeCun, director of AI Research at Facebook. LeCun was quoted saying the Star Wars bit was "a nice surprise." Huang's other surprise was a bit more practical, and showed just how competitive the AI chip market niche has become.
Any sufficiently complicated machine learning system contains an ad-hoc, informally-specified, bug-ridden, slow implementation of half of a programming language.1 As programming languages (PL) people, we have watched with great interest as machine learning (ML) has exploded – and with it, the complexity of ML models and the frameworks people are using to build them. State-of-the-art models are increasingly programs, with support for programming constructs like loops and recursion, and this brings out many interesting issues in the tools we use to create them – that is, programming languages. While machine learning does not yet have a dedicated language, several efforts are effectively creating hidden new languages underneath a Python API (like TensorFlow) while others are reusing Python as a modelling language (like PyTorch). We'd like to ask – are new ML-tailored languages required, and if so, why?
Machine learning (ML) powers an increasing number of the applications and services that we use daily. For organizations who are beginning to leverage datasets to generate business insights, the next step after you've developed and trained your model is deploying the model to use in a production scenario. That could mean integration directly within an application or website, or it may mean making the model available as a service. As ML continues to mature, the emphasis starts to shift from development towards deployment, you need to transition from developing models to real-world production scenarios that are concerned with issues of inference performance, scaling, load balancing, training time, reproducibility, and visibility. In previous posts, we've explored the ability to save and load trained models with TensorFlow that allow them to be served for inference.
"Amazon has a long history of machine learning" Amazon Web Services (AWS) is looking to bring machine learning (ML) to ordinary developers, launching the SageMaker service to simplify building applications, reports Enterprise Cloud News (Banking Technology's sister publication). ML is too complicated for ordinary developers, AWS CEO Andy Jassy said at a keynote during the AWS re:Invent event. "If you want to enable most enterprises and companies to be able to use ML in an expansive way, we have to solve the problem of making it accessible to everyday developers and scientists," he said. Amazon has a long history of ML, Jassy says. "We've been doing ML at Amazon for 20 years," he said.