When dealing with audio files a lot of work is required to index them properly so that is easy to lookup for them when needed. In this example we'll show how to use IBM Watson Speech to Text to recognize speech from audio files stored on Box and enrich their metadata with the extracted text. Go to Service Credentials and copy the username & password values, we're going to use them soon. In less then 1 minute you can have this up and running by using the project Blueprint, a pre-built template to help you get started with proven integration solutions. To get started with the Blueprint just click here.

I am trying to reproduced result from the paper "DYNAMIC COATTENTION NETWORKS FOR QUESTION ANSWERING" (https://arxiv.org/abs/1611.01604). I have implemented the code in pytorch but it is overfitting. In the paper it is mention that the authors use dropout for regularization. I added dropout and it helps a bit but not too much. I am also curious if it is possible to get feedback on my model code.

Recently I've been working on a customer service chatbot based on IBM Watson Assistant (formerly known as "IBM Watson Conversation Service") for a large Austrian telecommuncation provider. The chatbot was trained to answer questions on the website and to lead the user to the right website section. It currently handles 60k-80k conversations per months and covers 25% of the customer service interactions. It happened several times that minor changes in the dialog design or training caused previously working dialogs to fail -- so we were in need of regression testing. With Botium it was possible to generate test cases from the IBM Watson Assistant workspace and setup automated testing within some minutes.

Watson services on IBM Cloud are a set of REST APIs. This makes them quite simple to be used as a piece of a solution within an application. It also means they need to be integrated with various other parts of the solution to allow your users to interact with your instance of Watson. With the launch of Watson Assistant, integrating with other channels (Facebook, Slack, Intercom) has never been easier. Building a skill for Alexa is possible with Watson.

We analyze state-of-the-art deep learning models for three tasks: question answering on (1) images, (2) tables, and (3) passages of text. Using the notion of \emph{attribution} (word importance), we find that these deep networks often ignore important question terms. Leveraging such behavior, we perturb questions to craft a variety of adversarial examples. Our strongest attacks drop the accuracy of a visual question answering model from $61.1\%$ to $19\%$, and that of a tabular question answering model from $33.5\%$ to $3.3\%$. Additionally, we show how attributions can strengthen attacks proposed by Jia and Liang (2017) on paragraph comprehension models. Our results demonstrate that attributions can augment standard measures of accuracy and empower investigation of model performance. When a model is accurate but for the wrong reasons, attributions can surface erroneous logic in the model that indicates inadequacies in the test data.

I have been writing code for a number of years now, but was finally bitten by the AI bug in 2016. A thrill of excitement ran through me as I ran demos of applications that were powered by AI. Seeing the potential and value of how AI could change our lives, I was convinced that AI was the future, only to find out later that I was wrong. AI had been a part of my life all along. It had worn several clothes like People You May Know on Facebook, autocorrect while I typed on my phone, Siri, and so many others.

Existing attention mechanisms either attend to local image grid or object level features for Visual Question Answering (VQA). Motivated by the observation that questions can relate to both object instances and their parts, we propose a novel attention mechanism that jointly considers reciprocal relationships between the two levels of visual details. The bottom-up attention thus generated is further coalesced with the top-down information to only focus on the scene elements that are most relevant to a given question. Our design hierarchically fuses multi-modal information i.e., language, object- and gird-level features, through an efficient tensor decomposition scheme. The proposed model improves the state-of-the-art single model performances from 67.9% to 68.2% on VQAv1 and from 65.3% to 67.4% on VQAv2, demonstrating a significant boost.

Use streaming IoT data to uncover insights that help you better understand equipment health. Gain real-time visibility into manufacturing and supply chain processes; and monitor overall plant performance, product quality and vehicle safety issues to mitigate or avoid costly product recalls.

The graph represents a network of 3,453 Twitter users whose tweets in the requested range contained "ibm watson", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 06 May 2018 at 03:42 UTC. The requested start date was Sunday, 06 May 2018 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 12-day, 10-hour, 28-minute period from Sunday, 22 April 2018 at 00:01 UTC to Friday, 04 May 2018 at 10:30 UTC.

Technology giants IBM and Salesforce are expanding their strategic partnership by bringing together their artificial intelligence and cloud computing platforms to help companies connect with customers and collaborate more effectively with deeper insights. Salesforce has named IBM as a preferred cloud services provider and IBM has named Salesforce as its preferred customer engagement platform for sales and service, the companies said in a release. "This expanded partnership builds on the combined power of Watson and Einstein to help enterprises make smarter business decisions," said Ginni Rometty, IBM's chairman, president and chief executive officer. Watson and Einstein are the artificial intelligence platforms of IBM and Salesforce respectively. As a part of this extended strategic partnership, IBM will build newIBM WatsonQuip Live Apps, bringing the power of Watson and Quip together.