N1005 IBM Research: IBM Watson

Watson: What the IoT has Been Waiting For

<br /><div class="separator" style="clear: both; text-align: center;"></div><div class="separator" style="clear: both; text-align: center;"><a href="https://c1.staticflickr.com/1/413/19687798544_128b7c7998_m.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://c1.staticflickr.com/1/413/19687798544_128b7c7998_m.jpg" /></a></div><i><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-bidi-font-family: Arial;">Editor's note<span style="font-family: "helvneue light for ibm" , sans-serif;">:</span> This article is by <a href="http://researcher.watson.ibm.com/researcher/view.php?person=zurich-cur">Dr. Alessandro Curioni</a>, Vice President, Europe, and Director, IBM Research - Zurich</span></i><br /><br /><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-bidi-font-family: Arial;">“How much does Watson cost?”</span><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;"></span><br /><br /><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-bidi-font-family: Arial;">I often get asked this question whenever I give a lecture or speak with clients about cognitive computing.</span><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;"></span><br /><br /><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-bidi-font-family: Arial;">My response is always the same<span style="font-family: "helvneue light for ibm" , sans-serif;">:</span> “What is the price of not knowing?” And depending on who I am speaking to, the context could be not knowing when the next devastating storm will arrive, what the next maintenance failure on an aircraft will be, or even what’s going to be the next fashion trend.</span><br /><a name="more"></a><br /><br /><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-bidi-font-family: Arial;">Right now, we don’t know a lot. Petabytes of data are being created every second<span style="font-family: "helvneue light for ibm" , sans-serif;"> of</span> every day, in large part by the <a href="http://www.ibm.com/IoT">Internet of Things</a> (IoT). Yet, 90<span style="font-family: "helvneue light for ibm" , sans-serif;"> percent</span> of it, which is often referred to as <i>data on the edge</i>, is never captured, analyzed or acted upon — it essentially can’<span style="font-family: "helvneue light for ibm" , sans-serif;">t</span> be read by computers. For example, only 1 percent of the data collected from an oil rig with 30,000 sensors is examined for anomalies. To me that translates into a lost opportunity, or worse a pending disaster.</span><br /><br /><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-bidi-font-family: Arial;">Today, I’m in Munich, Germany, where IBM is changing all of this as we bet big on a future IoT powered by IBM Watson and cognitive computing. Munich is where this new business unit will be headquartered, bringing together 1,000 IBM developers, consultants, designers and researchers — representing IBM’s largest investment in Europe in more than two decades. </span><span style="font-family: "ms gothic"; font-size: 13.0pt; mso-bidi-font-family: "MS Gothic";"> </span><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-bidi-font-family: Arial;">As the vice president for Research in Europe and director of IBM’s lab in Zurich, I’ll be supporting this new unit globally to drive IoT research across all 12 of our global labs.</span><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;"></span><br /><br /><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-bidi-font-family: Arial;">Of course we aren’t starting from scratch. In March 2015, we announced a $3B commitment to IoT. <span style="font-family: "helvneue light for ibm" , sans-serif;">M</span>ore recently we announced plans to acquire the Weather Company’s IT assets<span style="font-family: "helvneue light for ibm" , sans-serif;">. It</span> includes a remarkable three billion weather forecast reference points, and a cloud platform supporting 26 billion inquiries per day, mostly from mobile apps. Combined with the dozens of Watson APIs available on IBM’s Watson IoT Cloud, this platform will bring the promise of IoT to both small businesses and large enterprises alike.</span><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; line-height: 107%;"> </span><br /><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; line-height: 107%;"> </span> <br /><a href="https://c2.staticflickr.com/6/5718/23177762070_72c8e2eb97.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="213" src="https://c2.staticflickr.com/6/5718/23177762070_72c8e2eb97.jpg" width="320" /></a><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-bidi-font-family: Arial;">In China, our <a href="http://www-03.ibm.com/press/us/en/pressrelease/48255.wss">Green Horizon Project</a> is using sensor data and analytics to generate high-resolution 1km-by-1km pollution forecasts 72 hours in advance<span style="font-family: "helvneue light for ibm" , sans-serif;">. It can also make </span>pollution trend predictions up to 10 days into the future to warn citizens and to help officials take action. Similar projects are taking place in <a href="http://www-03.ibm.com/press/us/en/pressrelease/48415.wss">South Africa</a>and <a href="http://www-03.ibm.com/press/us/en/pressrelease/48416.wss">India</a>as well.</span><br /><br /><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-bidi-font-family: Arial;">Back home in Switzerland, my colleagues in Zurich are working with IBM researchers in Dublin to bring the IoT to automobiles. Cars make excellent sensor platforms since they have strong batteries and are idle 95<span style="font-family: "helvneue light for ibm" , sans-serif;"> percent</span> of the day. The sensors can be used to find available parking spaces or perhaps even more critically, they can detect gas leaks in a city's<span style="font-family: "helvneue light for ibm" , sans-serif;"> </span>infrastructure. </span><br /><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-bidi-font-family: Arial;"><br />So, what is the price of using cognitive computing and IoT to predict a gas leak before it can do any harm? Priceless.</span><br /><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-bidi-font-family: Arial;"><br /><i>Follow Alessandro on Twitter <a href="https://twitter.com/Ale_Curioni">@Ale_Curioni</a></i></span><br /><br /><a href="http://www.pinterest.com/pin/create/extension/?url=https%3A%2F%2Fwww.blogger.com%2Fblogger.g%3FblogID%3D3821464279005499761%23editor%2Ftarget%3Dpost%3BpostID%3D5362970009364900284%3BonPublishedMenu%3Dallposts%3BonClosedMenu%3Dallposts%3BpostNum%3D0%3Bsrc%3Dlink&media=https%3A%2F%2Fc2.staticflickr.com%2F6%2F5718%2F23177762070_72c8e2eb97.jpg&xm=h&xv=sa1.37.01&xuid=tYDKQnV6ndWa&description=" style="background-color: transparent; background-image: url(data:image/png; border: none; cursor: pointer; display: none; height: 20px; left: 359px; opacity: 0.85; position: absolute; top: 1236px; width: 40px; z-index: 8675309;"></a><a href="http://www.pinterest.com/pin/create/extension/?url=https%3A%2F%2Fwww.blogger.com%2Fblogger.g%3FblogID%3D3821464279005499761%23editor%2Ftarget%3Dpost%3BpostID%3D5362970009364900284%3BonPublishedMenu%3Dallposts%3BonClosedMenu%3Dallposts%3BpostNum%3D0%3Bsrc%3Dlink&media=https%3A%2F%2Fc2.staticflickr.com%2F6%2F5718%2F23177762070_72c8e2eb97.jpg&xm=h&xv=sa1.37.01&xuid=tYDKQnV6ndWa&description=" style="background-color: transparent; background-image: url(data:image/png; border: none; cursor: pointer; display: none; height: 20px; left: 359px; opacity: 0.85; position: absolute; top: 1236px; width: 40px; z-index: 8675309;"></a>

Editor's note : This article is by Dr. Alessandro Curioni , Vice President, Europe, and Director, IBM Research - Zurich “How much does ...

Accelerating Watson’s Performance with OpenPOWER

9:01 PM N1000 0

<br /><div class="separator" style="clear: both; text-align: center;"><a href="http://asmarterplanet.com/files/2014/02/rob_high.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="http://asmarterplanet.com/files/2014/02/rob_high.jpg" /></a></div><i><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">Editor's note: This article is by Rob High, IBM Fellow and IBM Watson CTO.</span></i> <br /><div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;"><br /></div><div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;"><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-fareast-font-family: "Times New Roman";">It’s only been four years since Watson played and won <i style="mso-bidi-font-style: normal;">Jeopardy!</i>against the very best players with a machine roughly the size of a master bedroom. Since then, we’ve completely re-engineered Watson’s appearance and abilities, delivering it via the cloud and incorporating revolutionary features that span language, speech, vision and data insights. <br /><br />With more than 30 cognitive services available via the </span><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;"><a href="http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/"><span style="color: #0082bf; mso-fareast-font-family: "Times New Roman";">Watson Developer Cloud</span></a></span><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-fareast-font-family: "Times New Roman";">on </span><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;"><a href="http://www.ibm.com/cloud-computing/bluemix/"><span style="mso-fareast-font-family: "Times New Roman";">Bluemix</span></a></span><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-fareast-font-family: "Times New Roman";">, we're bridging the gap between technology and today’s leading industries, accelerating how doctors, lawyers, marketers and other professionals analyze high volumes of data and glean critical insights to improve decision-making and to generate new ideas.</span><br /><a name="more"></a><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-fareast-font-family: "Times New Roman";"><br />Watson’s ability to recognize patterns of text, speech and vision that convey meaning, to understand complex questions through natural language processing, and to continually learn, unlocks the potential of unstructured data across a multitude of industries and disciplines. Watson’s speed and efficiency have always hinged on the quality of its large scale machine learning algorithms. <br /><br />Today, at IBM and OpenPOWER’s Accelerating Innovation event in Austin, we previewed the next iteration of our cognitive computing system architecture, integrating the NVIDIA Tesla Accelerated Computing Platform into Watson’s core technologies. The incorporation of NVIDIA flagship Tesla K80 GPU accelerators – coupled with the Watson’s POWER-based architecture – accelerates Watson’s retrieve and rank capabilities to 1.7x of its normal speed. This fuels our drive to further improve our cost-performance of Watson's cloud-based services. <br /><br />For example, if a call center agent is responding to an individual’s health and insurance query, the agent will be able to leverage Watson’s natural language processing technology to obtain an answer in real-time even faster and cheaper than before.<br /><br />In addition to bolstering response time, the GPU acceleration also increases Watson’s processing power to 10x its prior performance. Watson often requires a lot of compute power and time to digest, annotate and index large quantities of data to prepare it to perform its cognitive tasks. NVIDIA’s Tesla GPUs will accelerate the time it takes the cognitive computing system to process this information to enable it to interact in natural language.<br /><br />The combination of IBM POWER architecture and NVIDIA’s Tesla Platform will also facilitate the expansion of Watson’s deep learning functionality. Hardware acceleration is integral to Watson’s ability to deeply reason about vision and speech recognition – in a short period of time.<br /><br /><table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhJhkVr1pTBGmDUzkd9UuyZOWIyOxG2s-YvEPK4IV0gmUAL06OaDKgYepgnXEMpijokYtW4IZQEybJTtFP84lW4PDNgrDOGqDro7-AJ90HObcjMqNIFlCyrizJiHb1fTkouuwkXSzTkiKU/s1600/WatsonGPU.jpg" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhJhkVr1pTBGmDUzkd9UuyZOWIyOxG2s-YvEPK4IV0gmUAL06OaDKgYepgnXEMpijokYtW4IZQEybJTtFP84lW4PDNgrDOGqDro7-AJ90HObcjMqNIFlCyrizJiHb1fTkouuwkXSzTkiKU/s320/WatsonGPU.jpg" width="239" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Watson on Ope<span style="font-family: "helvneue light for ibm" , sans-serif;">nPOWER at </span>Accelerator Day</td></tr></tbody></table>This collaborative effort is a result of the </span><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;"><a href="http://openpowerfoundation.org/"><span style="mso-fareast-font-family: "Times New Roman";">OpenPOWER Foundation</span></a></span><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-fareast-font-family: "Times New Roman";">, a global open development organization formed by IBM, Google, NVIDIA, Mellanox and Tyan to facilitate innovation on POWER architecture and develop collaborative hardware and software solutions.<br /><br />By accelerating Watson’s response time and training capabilities through open innovation, OpenPOWER is also propelling our Watson Ecosystem – which has grown to more than <span style="font-family: "helvneue light for ibm" , sans-serif;">77</span>,000 developers globally and</span><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-bidi-font-family: Arial; mso-fareast-font-family: "Times New Roman";"> </span><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-fareast-font-family: "Times New Roman";"><span style="font-family: "helvneue light for ibm" , sans-serif;">350</span>startups and established businesses currently commercializing their products and services embedded with sophisticated cognitive computing APIs.<br /><br />Today’s demo in Austin was simply an appetizer, a preview of one of several Watson capabilities that we’re collectively accelerating. We will continue to optimize Watson services with OpenPOWER’s advanced architecture to give developers and clients a competitive edge in today’s cognitive era.<br /><br />One innovation at a time.<br style="mso-special-character: line-break;" /> </span><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;"></span></div><div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;"><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;"><span style="mso-tab-count: 1;"><br /></span></span></div>

bluemix ibm research-Austin IBM Watson openpower Accelerating Watson’s Performance with OpenPOWER

Editor's note: This article is by Rob High, IBM Fellow and IBM Watson CTO. It’s only been four years since Watson played and won Jeopar...

A rapid rate of discovery

2:26 PM N1000 0

<br /><div class="MsoNormal"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiDjeQc_6QE3b7z9hI6AweKNN0ouzaZNp5svLzqND9rjU6nrIrO_1dfeqSXvyjYEcHAVgU9WsomHHRjtyPra0qDgKbtXZe7UqJOBGGS9eAHvkYOoZJZNqMKMW7_g_o7p4X1x7CIZ-U5ZgAJ/s1600/discovery_advisory.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiDjeQc_6QE3b7z9hI6AweKNN0ouzaZNp5svLzqND9rjU6nrIrO_1dfeqSXvyjYEcHAVgU9WsomHHRjtyPra0qDgKbtXZe7UqJOBGGS9eAHvkYOoZJZNqMKMW7_g_o7p4X1x7CIZ-U5ZgAJ/s1600/discovery_advisory.jpg" /></a></div><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%;">The average scientific paper is, well, long. And a new one is published every 30 seconds. Even the world’s best speed readers can’t keep up. Thanks to </span><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%;"><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%;">the IBM Watson and IBM Research-Almaden teams,</span> as well as partners like the <a href="https://www.bcm.edu/news/research/automated-reasoning-hypothesis-generation">Baylor College of Medicine</a>, Watson Discovery Advisor can do all that page turning – learning and understanding along the way – to help life science and pharmaceutical experts access and understand the latest and most-relevant research related to their domain of expertise.</span><br /><a name="more"></a></div><div class="MsoNormal"><br /><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%;">Because Watson can read millions of documents in seconds, Discovery Advisor can cross-reference things like drug interaction and disease symptoms – and return not just every piece of related evidence, but visualizations that explain trends in the data, and offer hypotheses for a researcher’s query.</span><br /><blockquote class="tr_bq"><i style="mso-bidi-font-style: normal;"><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%;">Together with domain experts we have trained Watson to recognize some of the fundamental concepts in life sciences research, such as genes, drugs, and diseases. Watson can understand the various names that these entities go by, including a wide range of international brand names and code numbers, so it can, in seconds, retrieve all the relevant evidence in the literature for an entity of interest.</span></i> </blockquote><blockquote class="tr_bq"><i style="mso-bidi-font-style: normal;"><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%;">More importantly, Watson understands the relationships between these entities by reading and interpreting the literature. For example, it can rapidly return all documentary evidence describing genes that regulate a disease like Alzheimer’s, and summarize this information with intuitive visualizations.</span></i> </blockquote><blockquote class="tr_bq"><i style="mso-bidi-font-style: normal;"><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%;">Watson can also reveal patterns in the evidence, and by comparing the entities make predictions about potential connections that aren’t currently described in literature. It’s constantly learning as we provide more and more expert data and literature to ingest and cross-reference. Life sciences is just one of the domains in which we’re already seeing huge potential for this kind of cognitive discovery advisor.</span></i> </blockquote><blockquote class="tr_bq"><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%;">– Richard Martin, Research Scientist on the Watson Discovery Advisor team</span></blockquote></div><div class="MsoNormal"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh02sFzW-guvhWWh6Re7iCNKnLduu8JIXQnhQAHBRIiUV0BRhBoxSCsjzZrs_jnHDMdMkX6a1yfFXF9spMHAWfkQNAbOVSiMD3uyskFr94XA1d44q6nZ2-P1zCeGCwsy9O4QCHVUYRuPRAS/s1600/richardmartin2.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh02sFzW-guvhWWh6Re7iCNKnLduu8JIXQnhQAHBRIiUV0BRhBoxSCsjzZrs_jnHDMdMkX6a1yfFXF9spMHAWfkQNAbOVSiMD3uyskFr94XA1d44q6nZ2-P1zCeGCwsy9O4QCHVUYRuPRAS/s1600/richardmartin2.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">IBM Research's Richard Martin (r) demonstrating Watson Discovery Advisor</td></tr></tbody></table><div class="separator" style="clear: both; text-align: center;"></div><br /><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%;">There are more than 70,000 scientific articles about the tumor-suppressing protein p53, often referred to as “the guardian of the genome.” The p53 protein reacts to the detection of genomic problems by increasing the expression of hundreds of other proteins to try and fix these issues, and can even instruct potentially harmful cells to destroy themselves.</span></div><div class="MsoNormal"><br /></div><div class="MsoNormal"><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%;">It gets these calls-to-arms from another set of proteins that chemically modify p53 in response to particular biological conditions. But when Baylor and IBM started work on the challenge of understanding p53, only 28 proteins that perform that specific “phosphorylation” modification on p53 had been discovered – over the last 35 years of scientific research. By putting Watson to the task, it was just a matter of weeks before the teams had discovered and validated five more potential candidates in laboratory experiments.</span></div><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%;"></span><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%; mso-ansi-language: EN-US; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US;"></span><br /><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%; mso-ansi-language: EN-US; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US;"> </span><iframe allowfullscreen="" frameborder="0" height="315" src="https://www.youtube.com/embed/CuLJYdyJSFo" width="560"></iframe>

cognitive computing cognitive era healthcare IBM Watson pharma A rapid rate of discovery

The average scientific paper is, well, long. And a new one is published every 30 seconds. Even the world’s best speed readers can’t keep up....

You know what I mean? Watson does.

11:48 AM N1000 0

<br /><div class="MsoNormal"><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%;">Watson knows the definition of “brake” – the device that stops something; or the act of stopping something. But what about the hundreds of ways that the car-driving public describes problems with their brakes to a mechanic? The brakes might “squeal,” or “judder.” Or maybe they’re “soft.” All of those descriptors could indicate a different issue that aren’t understood by a computer. Until now. Thanks to IBM Research’s Dan Gruhl, Watson can help the mechanic and the car manufacturer pinpoint the problem, based on common, even slang descriptions.</span><br /><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%;"></span><br /><a name="more"></a></div><div class="MsoNormal"><table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhnhUbSefx7J46pHsHQ5paZ7Hp6CXOjvfF0vlChF42O5zuHMpk_jDq2BjETAw3y3G6HF5TdtMrSxnD3T2Bvnbb2mJvlxbNy_YXpHEmsjspPDVkd-BAJ05lqntHxt_M4aHRbWuLnd_9QBiHI/s1600/dangruhl.jpg" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhnhUbSefx7J46pHsHQ5paZ7Hp6CXOjvfF0vlChF42O5zuHMpk_jDq2BjETAw3y3G6HF5TdtMrSxnD3T2Bvnbb2mJvlxbNy_YXpHEmsjspPDVkd-BAJ05lqntHxt_M4aHRbWuLnd_9QBiHI/s320/dangruhl.jpg" width="239" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Dan Gruhl at 2015 Cognitive Colloquim</td></tr></tbody></table><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%;">Watson Concept Expansion, on demonstration at the Cognitive Computing Colloquium in San Francisco, expanded the cognitive system’s dictionary of related words, concepts, euphemisms, and colloquialisms to better-understand context across any industry or field. Dan’s team feeds Watson unstructured data from blogs and news articles, to Twitter’s firehose, among other sources.</span><br /><br /><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%;">Then, because this is a human-machine partnership, meant to augment one another’s intelligence, the human expert begins to tell Watson what is relevant (squeaky brakes), and not (I need a break). While Watson gathers and organizes by relevance online documents and discussions about how to fix various kinds of brake problems.</span><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%;"> </span><br /><br /><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%;">“It's a game of patterns. If I start with 'apple' and 'blackberry' in the tool, it needs to interact with me a few times before it realizes I am talking about fruits and not cell phone manufacturers.” Dan said.</span><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%;"> </span><br /><div class="MsoNormal"><br /></div><div class="MsoNormal"><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%;">That context from such a wide and diverse source that only Watson could gather and comprehend (in seconds) gives domain experts a new way to understand their industry, brand, or product. Try it out on <a href="http://concept-expansion-demo.mybluemix.net/">Bluemix, now</a>.</span></div></div><div class="MsoNormal"><br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiw2Bl6qPRnngapp4_PQUAMhIkyA7QxPCuGSMmvoilOQ9d6EKBR2Xb1PwyI0mt3E7MUioJyxvvFNmgsIK7ItuRhWBXqs7WZLewygBJJeHpl6XC6_z_14F3rJ_OobI3IbMzVB4gBmXXyZqmJ/s1600/content_expansion.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiw2Bl6qPRnngapp4_PQUAMhIkyA7QxPCuGSMmvoilOQ9d6EKBR2Xb1PwyI0mt3E7MUioJyxvvFNmgsIK7ItuRhWBXqs7WZLewygBJJeHpl6XC6_z_14F3rJ_OobI3IbMzVB4gBmXXyZqmJ/s1600/content_expansion.jpg" /></a></div><span style="font-family: "HelvNeue Light for IBM",sans-serif; font-size: 13.0pt; line-height: 107%;"> </span> </div>

bluemix cognitive computing cognitive era IBM Watson machine learning You know what I mean? Watson does.

Watson knows the definition of “brake” – the device that stops something; or the act of stopping something. But what about the hundreds of w...

Cognitive Computing for better decision making

8:38 AM N1000 0

<br /><div class="MsoNormal"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh89YTDRNCWQJfza_vO4O9o-lwhDCha11ztNN371hcjdyd8CQtERz3FCrB0dGmWCEHqSMhLXFnvlIksJN5gmE7YK3WMZySm4Fb0s7LfS6WNdkHJFerxf2UNQNUroIOU8zcF_EMhLmbdwOU/s1600/Lea.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh89YTDRNCWQJfza_vO4O9o-lwhDCha11ztNN371hcjdyd8CQtERz3FCrB0dGmWCEHqSMhLXFnvlIksJN5gmE7YK3WMZySm4Fb0s7LfS6WNdkHJFerxf2UNQNUroIOU8zcF_EMhLmbdwOU/s1600/Lea.jpg" /></a><span style="font-family: Verdana,sans-serif;"><i><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Editor's note: This article is by </span></i><i style="mso-bidi-font-style: normal;"><span lang="EN-US" style="color: black; font-size: 12.0pt; line-height: 107%; mso-bidi-font-family: Arial; mso-fareast-language: EN-IE;"><a href="http://researcher.ibm.com/researcher/view.php?person=ie-lea.deleris">Léa<span style="mso-fareast-language: EN-US;"> Deleris</span></a></span></i><i><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">, manager of Risk Management at IBM Research-Ireland. Additional contributions made by <a href="http://researcher.ibm.com/researcher/view.php?person=ie-CHARLESJ">Charles Jochim</a>, research staff member at IBM Research-Ireland.</span></i></span></div><div class="MsoNormal"><span style="font-family: Verdana,sans-serif;"><i><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">  </span></i></span></div><span style="font-family: Verdana,sans-serif;"> </span><br /><div class="MsoNormal" style="margin-bottom: .0001pt; margin-bottom: 0cm;"><br /><span style="font-family: Verdana,sans-serif;"><b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Alice:</span></b><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;"> `Would you tell me, please, which way I ought to go from here?'</span></span></div><span style="font-family: Verdana,sans-serif;"> </span><br /><div class="MsoNormal" style="margin-bottom: .0001pt; margin-bottom: 0cm;"><span style="font-family: Verdana,sans-serif;"><b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Cheshire Cat: `</span></b><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">That depends a good deal on where you want to get to.' </span></span></div><span style="font-family: Verdana,sans-serif;"> </span><br /><div class="MsoNormal" style="margin-bottom: .0001pt; margin-bottom: 0cm;"><span style="font-family: Verdana,sans-serif;"><b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Alice:</span></b><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;"> `I don't much care where—‘</span></span></div><span style="font-family: Verdana,sans-serif;"> </span><br /><div class="MsoNormal" style="margin-bottom: .0001pt; margin-bottom: 0cm;"><span style="font-family: Verdana,sans-serif;"><b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Cheshire Cat: `</span></b><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Then it doesn't matter which way you go.’</span></span></div><span style="font-family: Verdana,sans-serif;"> </span><br /><div class="MsoNormal" style="margin-bottom: .0001pt; margin-bottom: 0cm;"><span style="font-family: Verdana,sans-serif;"><b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Alice:</span></b><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;"> ‘So long as I get somewhere.’</span></span></div><span style="font-family: Verdana,sans-serif;"> </span><br /><div class="MsoNormal"><span style="font-family: Verdana,sans-serif;"><b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Cheshire Cat: </span></b><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">`Oh, you're sure to do that, if you only walk long enough.'</span></span><br /><a name="more"></a><br /><br /><span style="font-family: Verdana,sans-serif;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">This dilemma, and Stanford’s Decision Analysis Professor Dr. Rob Howard, who presented the <a href="https://www.cs.cmu.edu/~rgs/alice-VI.html">Alice in Wonderland</a>concept of indifference in a lecture, inspired me to study how mathematics can apply to the risk, uncertainty, and personal preferences that influence the decisions we make every day, about everything. What Alice and the Cheshire Cat so eloquently illustrate is that preferences are not as obvious as they may seem. I wanted to know, as a PhD student sitting among my fellow classmates, could natural language processing and cognitive computing be applied to web applications that could in turn, help us make more logical decisions?</span></span><br /><br /><span style="font-family: Verdana,sans-serif;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Fast forward to today, and from student to IBM research scientist, and I’m applying artificial intelligence to our human intelligence – and how we can debate with machines to help us make good decisions when facing uncertainty. Here’s how I explained it to a <a href="https://www.youtube.com/watch?v=RGUvQdcMmZk">TEDxParis</a>audience in February.</span></span></div><div class="MsoNormal"><br /></div><iframe allowfullscreen="" frameborder="0" height="315" src="https://www.youtube.com/embed/RGUvQdcMmZk" width="560"></iframe><span style="font-family: Verdana,sans-serif;"></span><br /><div class="MsoNormal"><span style="font-family: Verdana,sans-serif;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Now my team in Dublin is using machines to help medical professionals make more rational decisions. The tool we developed, called <a href="http://www.research.ibm.com/labs/ireland/research_areas/information_extraction.html">MedicalRecap</a>, extracts information from </span><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%; mso-bidi-font-family: Arial;"><a href="http://www.ncbi.nlm.nih.gov/pubmed" target="_blank">PubMed</a>’s 24 million online </span><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">citations to create a risk model for doctors.</span></span><br /><br /><span style="font-family: Verdana,sans-serif;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%; mso-bidi-font-family: Arial;">MedicalRecap’s semantic module allows doctors to cluster the extracted terms (variables of the risk model) by grouping similar or related terms into concepts. It also has an aggregation module, which allows the user to combine the extracted dependence and probability statements into a dependence graph, also known as a <a href="https://en.wikipedia.org/wiki/Bayesian_network">Bayesian network</a>.</span><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;"> </span></span><br /><br /><span style="font-family: Verdana,sans-serif;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Imagine an instance of a doctor needing to understand the role of tea and coffee consumption on the incidence of endometrial cancer. Currently, doctors would address this task manually by searching for relevant papers, reading them, taking notes (by hand or copy-pasting on a spreadsheet), and aggregating this data.</span></span><br /><br /><span style="font-family: Verdana,sans-serif;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">MedicalRecap, instead, presents extracted and aggregated data in an intuitive graphical format, providing a way for the user to trace back through the summarised information, to the original input. The tool also allows users to edit the output of the algorithms if they encounter an error, which is fed back into the system to improve its knowledge and performance over time.</span><span style="font-size: 12.0pt; mso-ansi-language: EN-IE; mso-bidi-font-family: Helv; mso-fareast-language: EN-IE;"> </span></span><br /><br /><span style="font-family: Verdana,sans-serif;"><span style="font-size: 12.0pt; mso-ansi-language: EN-IE; mso-bidi-font-family: Helv; mso-fareast-language: EN-IE;">MedicalRecap also relies on the doctor's expertise, so ideally, errors are reduced by combining the doctor's knowledge with the inferences the tool makes in finding dependency relationships. </span><span style="font-size: 12.0pt; mso-ansi-language: EN-IE; mso-bidi-font-family: "Tms Rmn"; mso-fareast-language: EN-IE;">The assumption is that the doctor does not have all the input required, but is exploring the space. The tool helps the practitioner look for answers, but does not provide them. Ideally, it will reach the same conclusions that the doctor already has made so that he or she will trust the system more.</span></span><br /><br /><iframe allowfullscreen="" frameborder="0" height="315" src="https://www.youtube.com/embed/fmfB4nU3j70" width="560"></iframe><br /><br /><span style="font-family: Verdana,sans-serif;"><span style="font-size: 12.0pt; mso-ansi-language: EN-IE; mso-bidi-font-family: "Tms Rmn"; mso-fareast-language: EN-IE;">We also want MedicalRecap to provide evidence for new conclusions to be drawn. For example, if the doctor sees that coffee consumption is linked to some cancers, which she already knew, the tool could show that in fact this is primarily for certain populations, which she didn't know.</span><span style="font-size: 12.0pt; line-height: 107%; mso-ansi-language: EN-IE; mso-bidi-font-family: "Tms Rmn"; mso-fareast-language: EN-IE;"><span style="mso-spacerun: yes;"> </span></span></span><br /><br /><span style="font-family: Verdana,sans-serif;"><span style="font-size: 12.0pt; line-height: 107%; mso-ansi-language: EN-IE; mso-bidi-font-family: "Tms Rmn"; mso-fareast-language: EN-IE;">As similar as it may sound, MedicalRecap is different to <a href="http://www.ibm.com/smarterplanet/us/en/ibmwatson/health/">IBM Watson Health</a>. Our tool is a web-based GUI focused only on published medical literature and is not designed for personalized medicine, but instead to make more global inferences between diseases and related risk factors. </span><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">But like Watson, MedicalRecap’s Extractor, Clusterer, and Aggregator services are</span><span style="color: black; font-size: 12.0pt; line-height: 107%; mso-ansi-language: EN-IE; mso-bidi-font-family: Calibri; mso-fareast-language: EN-IE;"> available on IBM’s <a href="http://www.softlayer.com/">SoftLayer</a> cloud infrastructure as a service.</span></span><br /><br /><span style="font-family: Verdana,sans-serif;"><span style="color: black; font-size: 12.0pt; line-height: 107%; mso-ansi-language: EN-IE; mso-bidi-font-family: Calibri; mso-fareast-language: EN-IE;">Our risk information extraction models, like in MedicalRecap, can be applied to other domains. </span><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">In the future, oil and gas experts could use the tool to extract information from academic papers about factors influencing reservoir capacity and shape.</span><span lang="EN-US" style="color: black; font-size: 12.0pt; line-height: 107%; mso-bidi-font-family: Arial; mso-fareast-language: EN-IE;"> As long as we have the main ingredient of a large body of literature related to a profession or domain, our decision support system tool might even be able to offer Alice somewhere to go, no matter how unsure she is.</span></span><b><span style="font-family: Verdana,sans-serif;"><span lang="EN-US" style="color: black; font-size: 12.0pt; line-height: 107%; mso-bidi-font-family: Arial; mso-fareast-language: EN-IE;">  </span></span></b></div><br /><div class="MsoNormal"><br /></div>

Cognitive Computing for better decision making

analytics cognitive computing IBM Watson MedicalRecap natural language processing semantic reasoning Cognitive Computing for better decision making

Editor's note: This article is by Léa Deleris , manager of Risk Management at IBM Research-Ireland. Additional contributions made by ...