[Sometimes called Case-Based Reasoning or CBR]
"At the highest level of generality, a general CBR cycle may be described by the following four processes: 1. RETRIEVE the most similar case or cases. 2. REUSE the information and knowledge in that case to solve the problem. 3. REVISE the proposed solution. 4. RETAIN the parts of this experience likely to be useful for future problem solving "– from Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. By A. Aamodt and E. Plaza. (1994)
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.
The Australian Department of Defence is using what Matt Smorhun, Assistant Secretary for the ICT Strategy Realisation Branch at the Department of Defence, said was only the second on-premises instance of Watson globally. Speaking at the CeBIT Australia conference in Sydney on Wednesday, Smorhun said the department decided to invest in a secret, classified version of IBM's Watson platform, instead of spending valuable time working out how to do artificial intelligence, cognitive learning, and advanced analytics on its own. The next wave of IT innovation will be powered by artificial intelligence and machine learning. We look at the ways companies can take advantage of it and how to get started. He said Defence decided to "just buy this thing" and then work out how it was going to fit into the organisation later.
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.
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.
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.
Qoints has launched an influencer marketing tool that uses the artificial intelligence of IBM's Watson to unearth the best influencers for your brand. The Qoints AI Social Discovery tool is a self-service tool that helps marketers deal with the problem of finding the right influencers for marketing campaigns. Locating the right micro influencers (those with 5,000 to 50,000 followers) is key to making influencer marketing effective and affordable. But it's a tedious process to manually search for the right influencers. Demand for influencers is growing, as they've been shown to generate higher levels of trust, engagement and purchase intent from their followers (in comparison to celebrity influencers), according to Toronto-based Qoints.
Following successful special tracks on case-based reasoning at FLAIRS over the past seven years, we invited papers for the Eighth Special Track on CBR at the 22nd International FLAIRS Conference. Case-based reasoning is an AI problem solving and analysis methodology that retrieves and adapts previous experiences to fit new contexts. This forum is intended to gather AI researchers and practitioners with an interest in CBR to present and discuss developments in CBR theory and application. Submission topics included foundations of CBR; methods for CBR (such as representation, indexing, retrieval, adaptation); evaluation methods for CBR systems and integrations; practical applications of CBR; textual CBR; CBR and creativity; CBR and design; distributed CBR; case based maintenance; spatiotemporal CBR; CBR in the health sciences; CBR integrations; case based planning; and CBR and games. The invited speaker for the special track for 2009 is Ashok Goel from the Georgia Institute of Technology, USA.