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IBM Enhances Watson's Natural Language Understanding Capabilities
IBM Corp. Said today it has made some big improvements to the natural language processing capabilities of its IBM Watson platform.
The new capabilities, which were born out of IBM Research's Project Debater, will help Watson understand and analyze some of the most challenging aspects of English language with greater clarity than before, the company said.
IBM's Project Debater is an artificial intelligence system that was built by the company to debate with humans on a complex range of topics. The system is designed to understand subtleties in the English language such as idioms and colloquialisms that traditional AI has always struggled with.
For example, a phrase such as "hot under the collar" is well-understood by humans, but most AI systems are likely to get the wrong end of the stick, so to speak, since their algorithms can't detect the true meaning. But those kinds of idioms aren't a problem for Project Debater, which is capable of much more advanced sentiment analysis.
IBM said it will be integrating Project Debater's natural language processing capabilities into Watson "throughout the year." Customers will then be able to add the new capabilities to their existing AI models to help them better exploit natural language.
"Language is a tool for expressing thought and opinion, as much as it is a tool for information," Rob Thomas, general manager of IBM Data and AI, said in a statement. "This is why we believe that advancing our ability to capture, analyze and understand more from language with NLP will help transform how businesses utilize their intellectual capital that is codified in data."
IBM said Project Debater adds four distinct capabilities to Watson, including more advanced sentiment analysis. As a result, it said, it can better identify and understand complicated word schemes like idioms and so-called "sentiment shifters" — combinations of words that, taken together, take on new meaning, such as "hardly helpful."
Watson will also gain the ability to pull textual data from multiple sources and provide users with a summary of what's said or written about a specific topic. There's also a new "clustering" feature that will enable Watson to cluster incoming data to create meaningful topics of related information that can later be analyzed. In turn, this will allow customers to customize each topic to reflect the language of specific industries and businesses, such as insurance, healthcare or manufacturing.
Finally, Watson will gain the ability to classify clauses that occur in business documents such as contracts from as few as several hundred samples.
IBM said the advanced sentiment analysis capabilities will be added to Watson next month, with the rest of the features to be rolled out throughout the course of the year.
Here's a demo of Project Debater's capabilities from IBM's Think 2019 event in San Francisco last year:
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IBM Advances Watson's Ability To Understand The Language Of Business
NEW YORK, March 11, 2020 /PRNewswire/ -- IBM (NYSE: IBM), the leader in artificial intelligence for business[1], is announcing several new IBM Watson technologies designed to help organizations begin identifying, understanding and analyzing some of the most challenging aspects of the English language with greater clarity, for greater insights.
The new technologies represent the first commercialization of key Natural Language Processing (NLP) capabilities to come from IBM Research's Project Debater, the only AI system capable of debating humans on complex topics. For example, a new advanced sentiment analysis feature is defined to identify and analyze idioms and colloquialisms for the first time. Phrases, like 'hardly helpful,' or 'hot under the collar,' have been challenging for AI systems because they are difficult for algorithms to spot. With advanced sentiment analysis, businesses can begin analyzing such language data with Watson APIs for a more holistic understanding of their operations. Further, IBM is bringing technology from IBM Research for understanding business documents, such as PDF's and contracts, to also add to their AI models.
"Language is a tool for expressing thought and opinion, as much as it is a tool for information," said Rob Thomas, General Manager, IBM Data and AI. "This is why we're harvesting technology from Project Debater and integrating it into Watson - to enable businesses to capture, analyze, and understand more from human language and start to transform how they utilize intellectual capital that's codified in data."
Today IBM is announcing that it plans to integrate Project Debater technologies into Watson throughout the year, with a focus on advancing clients' ability to exploit natural language:
A. Analysis - Advanced Sentiment Analysis. IBM has enhanced sentiment analysis to be able to better identify and understand complicated word schemes like idioms (phrases and expressions) and so called, sentiment shifters, which are combinations of words that, together, take on new meaning, such as, "hardly helpful." This technology will be integrated into Watson Natural Language Understanding this month. In addition, we are announcing a new classification technology that will enable clients to create AI models that can more easily classify clauses that occur in business documents, like procurement contracts. Based on Project Debater's deep learning-based classification technology, the new capability can learn from as few as several hundred samples to do new classifications quickly and easily. It is planned to be added to Watson Discovery later this year.
B. Briefs - Summarization. This technology pulls textual data from a variety of sources to provide users with a summary of what is being said and written about a particular topic. An early version of Summarization was leveraged at The GRAMMYS this year to analyze over 18 million articles, blogs and bios to produce bite-sized insights on hundreds of GRAMMY artists and celebrities. The data was then infused into the red carpet live stream, on-demand videos and photos across www.Grammy.Com to give fans deeper context about the leading topics of the night. It is planned to be added to IBM Watson Natural Language Understanding later in the year.
C. Clustering - Advanced Topic Clustering. Building on insights gained from Project Debater, new topic clustering techniques will enable users to "cluster" incoming data to create meaningful "topics" of related information, which can then be analyzed. The technique, which is planned to be integrated into Watson Discovery later this year, will also allow subject matter experts to customize and fine-tune the topics to reflect the language of specific businesses or industries, like insurance, healthcare and manufacturing.
IBM, has long been a leader in NLP, developing technologies that enable computer systems to learn, analyze and understand human language - including sentiment, dialects, intonations, and more - with increasing accuracy and speed. IBM has brought its NLP technology, much of which was born in IBM Research, to market via Watson. Product such as, Watson Discovery for document understanding, IBM Watson Assistant for virtual agents, and Watson Natural Language Understanding for advanced sentiment analysis, are all infused with NLP.
ESPN Fantasy Football uses Watson Discovery and Watson Knowledge Studio to analyze millions of football data sources each day during the season to offer millions of fantasy football players real-time insights. By processing natural language, Watson identifies the tone and sentiment of news articles, blogs, forums, rankings, projections, podcasts and tweets that cover everything from locker room insights to injury analysis. ESPN Fantasy Football surfaces these insights in player cards that snapshot the "boom" and "bust" potential of each player, as well as a "Player Buzz" section that summarizes the positive or negative commentary about a player.
KPMG, a multinational professional services network, and one of the Big Four accounting organizations, worked with IBM to create an AI solution based on a variety of Watson services, including Watson Natural Language Understanding. This technology makes it more effective for companies to identify, claim and retain potential R&D income tax credits. Developed by KPMG, the solution can help clients increase the amount of R&D income tax credits they capture because the Watson technology is able to review more documentation quickly while minimizing disruption to the client's business.
In the past year, KPMG clients have seen more potential for R&D tax credits, with some projects even seeing more than a 1000% increase in the number of documents reviewed. The solution helps clients uncover more potential activities that qualify for additional income tax credits, while reducing business disruption. As a result, engineers and scientists can stay focused on innovative R&D work by spending less time on income tax compliance activities.
Watch 'The Debater'
Check out the trailer for The Debater, a rare, behind-the-scenes look at the making of Project Debater though the lens of an eclectic team of researchers that dare to take AI into uncharted territory. Official Selection of the Copenhagen International Documentary Film Festival www.Ibm.Com/research/debater-film.
About IBM For more information please visit IBM Watson.For more information please visit IBM Research.Developers and data scientists can access Watson APIs at IBM Developer. Register here to view a webinar on the news which will be broadcast on March 18 at 1:00PM ET.
Forward-Looking and Cautionary StatementsExcept for the historical information and discussions contained herein, statements contained in this release may constitute forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. Forward-looking statements are based on the company's current assumptions regarding future business and financial performance. These statements involve a number of risks, uncertainties and other factors that could cause actual results to differ materially, including the following: a downturn in economic environment and client spending budgets; the company's failure to meet growth and productivity objectives; a failure of the company's innovation initiatives; damage to the company's reputation; risks from investing in growth opportunities; failure of the company's intellectual property portfolio to prevent competitive offerings and the failure of the company to obtain necessary licenses; cybersecurity and data privacy considerations; fluctuations in financial results, impact of local legal, economic, political and health conditions; adverse effects from environmental matters, tax matters and the company's pension plans; ineffective internal controls; the company's use of accounting estimates; the company's ability to attract and retain key employees and its reliance on critical skills; impacts of relationships with critical suppliers; product quality issues; impacts of business with government clients; currency fluctuations and customer financing risks; impact of changes in market liquidity conditions and customer credit risk on receivables; reliance on third party distribution channels and ecosystems; the company's ability to successfully manage acquisitions, alliances and dispositions; risks from legal proceedings; risk factors related to IBM securities; and other risks, uncertainties and factors discussed in the company's Form 10-Qs, Form 10-K and in the company's other filings with the U.S. Securities and Exchange Commission (SEC) or in materials incorporated therein by reference. Any forward-looking statement in this release speaks only as of the date on which it is made. The company assumes no obligation to update or revise any forward-looking statements.
Contact Michael ZimmermanIBM Media Relationsmrzimmerman@us.Ibm.Com
[1]IDC Market Share: Worldwide Artificial Intelligence Market Shares, 2018: Steady Growth — POCs Poised to Enter Full-Blown Production (Doc # US45334719, July 2019)
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SOURCE IBM
IBM Talks Up Deeper Language Smarts For Watson - CIO
IBM has announced significant updates to Watson Natural Language Understanding that it said will enable organisations to better gauge customer sentiment, as well as achieve faster and more accurate processing of documents such as PDFs at scale.
It amounts to the commercial debut of key IBM Natural Language Processing (NLP) capabilities emerging from its 'Project Debator', which it says is the only AI system able to engage humans in complex debates.
New features will be added progressively throughout this year, the company said.
Among the key capabilities now being brought to market with Watson is the ability to understand idioms and colloquialisms such as 'hardly helpful' or 'hot under the collar', which have tended to confuse AI systems to date.
Such phrases are known as 'sentiment shifters', and with advanced sentiment analysis, businesses can now make better use of this data to gain a more holistic understanding of operations.
In addition to advanced sentiment analysis, IBM is also adding new capabilities for 'summarization' as well as 'clustering'.
Summarization pulls textual data from multiple sources providing users with an overview of what is being said and written about a particular topic. A previous version of this technology was deployed earlier this year to analyse over 18 million articles, blogs and bios as part of a large-scale data project undertaken for the Grammys music awards. Data was used to build a live stream for the red carpet segment with on-demand videos and photos giving fans deeper context on leading topics.
New topic clustering techniques will enable users to create meaningful "topics" of related information for analysis. IBM said the technique will be integrated into Watson Discovery later this year, and will also allow subject matter experts to customise and tweak topics to reflect the language of different businesses or industries such as insurance, healthcare and manufacturing.
"Language is a tool for expressing thought and opinion, as much as it is a tool for information," said Rob Thomas, general manager, IBM Data and AI.
"This is why we're harvesting technology from Project Debater and integrating it into Watson – to enable businesses to capture, analyse, and understand more from human language and start to transform how they utilise intellectual capital codified in data."
IBM already has a number of high profile customers using AI-driven language processing in Australia, including financial services company, Suncorp.
Jason Leonard, partner, cognitive computing and analytics at IBM Australia told CIO the new language features announced for IBM Watson would enable significant improvements in sentiment analysis across key areas of their operations.
Already Suncorp has reported encouraging results in its insurance claims division, using a Watson-based system able to understand simple yet important distinctions such as "the car crashed into the back of me" and "I crashed into the back of the car".
Lisa Harrison, Suncorp's chief customer and digital officer said the company has been using Watson natural language to improve customer experiences since 2017, as well as simplifying processes freeing staff to work on more meaningful tasks.
"Each year we receive more than 500,000 motor insurance claims and implementing this technology has significantly reduced the time taken for customers to complete a claim, with many doing so in as little as five minutes," she said.
"IBM Watson is smart, fast and reliable, and we are excited to seeing how the new NLP features will enable us to deliver more great outcomes for our customers."
Other notable Australian customers include energy giant Woodside, which is using Watson language processing to collect and analyse language data for improving worker safety, while Sydney-based media company Oovoo is using Watson language processing to vastly speed up the process of selecting videos to accompany articles online.
Internationally, ESPN Fantasy Football has deployed Watson Discovery and Watson Knowledge Studio to crunch millions of football data sources each day of the season, providing millions of fantasy football players with real-time insights.
Watson uses natural language processing to discern the tone and sentiment of multiple data sources including news articles, forums, blogs, projections, rankings, tweets and podcasts that might cover everything from locker-room chatter to injury analysis.
ESPN Fantasy Football then "surfaces" these discoveries in so-called player cards that note the "boom and bust" potential of players, while also informing the 'Player Buzz" section which summarises positive or negative commentary about a player.
A natural extension of IBM's infamous foray into the Jeopardy game show world, Debator has also proved itself a capable adversary with a recent debate being only narrowly won by a human according to a live audience, which voted the 'machine' as a better source of knowledge.
It's an interesting development for industries and professions where language and communication are prominent.
"We can see this technology playing an important role in the legal profession where machines could be used to augment the activities of lawyers actually presenting in court," said IBM's Leonard.
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