Published By: IBM APAC
Published Date: Nov 22, 2017
Using IBM Watson’s cognitive capabilities, companies can quickly differentiate their customer service quality by being more pro active and responsive to customer needs. Simply put, chatbots and virtual agents are the future of customer interactions. Building apps from scratch that incorporate natural language processing, speech to text recognition, visual recognition, analytics, and artificial intelligence requires broad expertise in these disciplines, large staffs, and a huge financial commitment. Making use of IBM Watson cognitive services brings these capabilities in-house quickly and without the capital investment that would be needed to develop the technologies within an organization.
Published By: IBM APAC
Published Date: Nov 22, 2017
AlchemyAPI’s approach to natural language processing incorporates both linguistic and statistical analysis techniques into a single unified system. This hybrid approach provides an industry-leading advantage since both techniques have benefits and drawbacks depending on the content and specific usecases. Linguistic analysis takes a basic grammatical approach to understand how words combine into phrases, and how those phrases combine into sentences. While this approach works well with editorialized text (e.g., news articles and press releases), it does not perform as well when it comes to usergenerated content, often filled with slang, misspellings and idioms. Statistical analysis, however, understands language from a mathematical standpoint and works well on “noisy” content (e.g., tweets, blog posts, and Facebook status updates). The combination of these two approaches allows for increased accuracy on a variety of content.
Artifi cial intelligence is becoming a key component of business transformation. Virtually any business leader seeking to unlock value and develop new capabilities using technology is at some stage of the AI journey. For example, those at the leading edge have incorporated machine learning insights into business processes and are building functionality such as natural language processing and preventative maintenance diagnostics into their products. Others are experimenting with pilot projects or developing plans to get started.
As organizations develop next-generation applications for the digital era, many are using cognitive computing ushered in by IBM Watson® technology. Cognitive applications can learn and react to customer preferences, and then use that information to support capabilities such as confidence-weighted outcomes with data transparency, systematic learning and natural language processing.
To make the most of these next-generation applications, you need a next-generation database. It must handle a massive volume of data while delivering high performance to support real-time analytics. At the same time, it must provide data availability for demanding applications, scalability for growth and flexibility for responding to changes.
"The appearance of your reports and dashboards – the actual visual appearance of your data analysis -- is important. An ugly or confusing report may be dismissed, even though it contains valuable insights about your data. Cognos Analytics has a long track record of high quality analytic insight, and now, we added a lot of new capabilities designed to help even novice users quickly and easily produce great-looking and consumable reports you can trust.
Watch this webinar to learn:
• How you can more effectively communicate with data.
• What constitutes an intuitive and highly navigable report
• How take advantage of some of the new capabilities in Cognos Analytics to create reports that are more compelling and understandable in less time.
• Some of the new and exciting capabilities coming to Cognos Analytics in 2018 (hint: more intelligent capabilities with enhancements to Natural Language Processing, data discovery and Machine Learning)."
Recognizing the shift to a subscription business model required real-time customer support, Autodesk turned to IBM technology to enhance its customer experience.
Using Watson Assistant, Autodesk developed a virtual agent to interact with customers, applying natural language processing (NLP) and deep learning techniques to recognize and extract the intent, context and meaning behind inquiries. Quickly resolving easy customer concerns, Watson Assistant is supporting 100,000 conversations per month, with response times 99% faster than before and leading to a 10-point increase in customer satisfaction levels for Autodesk.
Find out how Watson Assistant can accelerate your customer support experience.
Click here to find out more about how embedding IBM technologies can accelerate your solutions’ time to market.
Moving Beyond Traditional Decision Support
Future-proofing a business has never been more challenging. Customer preferences turn on a dime, and their expectations for service and support continue to rise. At the same time, the data lifeblood that flows through a typical organization is more vast, diverse, and complex than ever before. More companies today are looking to expand beyond traditional means of decision support, and are exploring how AI can help them find and manage the “unknown unknowns” in our fast-paced business environment.
Zoom out to the bigger picture, though, and you see that Facebook is just one channel. If you use Skype, Slack, Kik, and digital voice assistants, you’ll have to build six or eight of these endpoints straight away. And chatbots are being asked to handle ever more complex responses, so you better build on a platform of machine learning and natural language processing to keep up.
That’s why the question enterprise developers should be asking is not “Which chatbot service do I start with?” but “Which platform will let me crank out a chatbot today and also support multiple channels and integrate with back-end systems as these chatbots take off?”
To address the volume, velocity, and variety of data necessary for population health management, healthcare organizations need a big data solution that can integrate with other technologies to optimize care management, care coordination, risk identification and stratification and patient engagement. Read this whitepaper and discover how to build a data infrastructure using the right combination of data sources, a “data lake” framework with massively parallel computing that expedites the answering of queries and the generation of reports to support care teams, analytic tools that identify care gaps and rising risk, predictive modeling, and effective screening mechanisms that quickly find relevant data. In addition to learning about these crucial tools for making your organization’s data infrastructure robust, scalable, and flexible, get valuable information about big data developments such as natural language processing and geographical information systems. Such tools can provide insig
Advances in deep neural networks have ignited a new wave of algorithms and tools for data scientists to tap into their data with artificial intelligence (AI). With improved algorithms, larger data sets, and frameworks such as TensorFlow, data scientists are tackling new use cases like autonomous driving vehicles and natural language processing. Read this technical white paper to learn reasons for and benefits of an end-to-end training system. It also shows performance benchmarks based on a system that combines the NVIDIA® DGX-1™, a multi-GPU server purpose-built for deep learning applications and FlashBlade, a scale-out, high performance, dynamic data hub for the entire AI data pipeline.
Learn more about Watson (as seen on Jeopardy!), the latest IBM Research Grand Challenge, designed to further the science of natural language processing through advances in question and answer technology. This paper explains Watson's workload optimized system design and why this represents a new computing paradigm.
Read this white paper to learn how marketers are using IBM technology to learn about their customers' attitudes, preferences and buying habits from what they say on publicly available social media and through the full range of interactions that can be recorded, measured and analyzed. Discover how marketers are combining that knowledge with other sources of customer information to guide marketing decisions and shape marketing campaigns, cultivating relationships with online advocates to help steer product development, and, ultimately, boosting sales and revenue.
LOOK TO RELEVANT USE CASES FOR YOUR BUSINESS. While use cases vary across industries, the most common ones fall into these categories and are usually associated with the listed neural network:
• Image classification or object detection: convolutional neural network (CNN) • Time-series predictions: long short-term memory (LSTM) • Natural language processing: recurrent neural network (RNN) • Unlabeled data classification and data labeling: autoencoder (AE) • Anomaly detection: autoencoder (AE) • Recommender systems: multilayer perceptron (MLP)
Work with your deep learning talent or consultants to identify which use cases best match your organization and desired solutions. Then recreate a successful, already proven method.
There is a lot of excitement in the market about artificial intelligence (AI), machine learning
(ML), and natural language processing (NLP). Although many of these technologies have been
available for decades, new advancements in compute power along with new algorithmic
developments are making these technologies more attractive to early adopter companies. These
organizations are embracing advanced analytics technologies for a number of reasons including
improving operational efficiencies, better understanding behaviors, and gaining competitive
Explaining what your company does can be a challenge. When your product is natural language processing, it can be outright difficult. Learn how the explainer video we produced for Inbenta helped to communicate their value to both customers and investors, but also increased online conversion rates by 20%.
Artificial intelligence (AI) leads the charge in the current
wave of digital transformation underway at many global
companies. Organizations large and small are actively
expanding their AI footprints as executives try to comprehend
more fully what AI is and how they can use it to capitalize
on business opportunities by gaining insight to the data
they collect that enables them to engage with customers
and hone a competitive edge. But, while AI may indeed be
the frontier of enterprise technology, there remain many
misconceptions about it.
Part of the confusion stems from the fact that AI is an
umbrella term that covers a range of technologies —
including machine learning, computer vision, natural language
processing, deep learning, and more — that are in various
stages of development and deployment. The use of AI for
dynamic pricing and targeted marketing has been in use for
a while, but actual AI computing where machines think like
humans is still many years from becoming mainstream. T
Life revolves around prediction—for example, the route you take to get to work, whether to go on a second date, or whether or not to keep reading this sentence are all forms of prediction. We are already seeing machine learning powered by Apache Spark changing the face of innovation at IBM. Learn more.
With all of the attention on machine learning, many are seeking a better understanding of this hot topic and the benefits that it could provide to their organizations. Machine learning – as well as deep learning, natural language processing and cognitive computing – are driving innovations in identifying images, personalizing marketing campaigns, genomics, and navigating the self-driving car. This e-book provides a primer on these innovative techniques as well as 10 best practices and a checklist for machine learning readiness.
Did you know that by 2020, 50% of analytic queries will be generated using search, natural-language processing or voice, or will be automatically generated?
Read the Gartner report Technology Insight for Modern Analytics and Business Intelligence Platforms and find out how to meet the time-to-insight demands of today's competitive business environment. Learn how to:
• Determine when to use existing, traditional BI technologies versus modern analytics and BI
• Broaden data access beyond relational systems
• Adopt new approaches to data modeling
Advances in deep neural networks have ignited a new wave of algorithms and tools for data scientists to tap into their data with artificial intelligence (AI). With improved algorithms, larger data sets, and frameworks such as TensorFlow, data scientists are tackling new use cases like autonomous driving vehicles and natural language processing.