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GUEST BLOG BY MICHELLE HUCHETTE:
My name is Michelle Huchette and I am a rising fourth year at the University of Virginia studying Computer Science and Statistics. This summer I was fortunate enough to be a part of the IBM Summit Program as a Technical Sales Intern. In this role I was able to experience what it is like to be an IBM seller by attending customer events and working on various tasks and projects over the course of 11 weeks. A few weeks ago I was challenged with creating a Proof of Technology lab that would interest customers in the field of machine learning. This is a brief overview of the creation and utilization of the model I created to diagnose breast cancer tumors.
The data set used for the lab was found in UC Irvine Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29) that contained information regarding breast cancer tumors and information to help predict the diagnoses of the tumors as malignant or benign. The data set contains 10 measurements of each cell nucleus captured using images of cell nuclei gathered from a fine needle aspiration procedure (FNA). The average, standard error, and extreme values of all nuclei in the tumor sample were calculated for each of the following features:
- Texture (standard deviation of gray-scale values)
- Smoothness (local variation in radius length)
- Compactness (perimeter2/area – 1)
- Concavity (severity of concave portions of the contour)
- Concave points (number of concave portions of the contour)
- Fractal dimension (coastline approximation – 1)
After finding a data set, a machine learning model could be created to diagnose breast cancer tumors. In order to do so we needed to set up a Watson Studio account on the IBM Cloud platform( https://console.bluemix.net/registration/ ). Within Watson Studio(https://console.bluemix.net/catalog/services/watson-studio ) we created a Jupyter Notebook which was used to write a python code to work with the data set, create a model, and make the predictions, all using Apache Spark as the analytics engine.
The first step to creating the machine learning model required determining which type of model would be the best fit for the data. Research found that there are different types of models that Spark supports such as Naïve Bayes, Decision Trees, Random Forests, and Regression Models, which are the most common. Because Naïve Bayes required a strong independence assumption between the features, that type of model was ruled out. Ultimately, a Logistic Regression Model was chosen since it is often used for models of binary categorical outcome (exactly what we’re dealing with when trying to diagnose a tumor as malignant or benign) and it is good at measuring the relationship between the labels and features.
To start out, the logistic regression model was set to have the default parameters so that an initial model could be created and improved upon if needed. Once the model was defined, a pipeline was set up which contained a sequence of stages to be run in a specific order. Within a pipeline each stage is either a transformer, which converts a dataframe into other dataframes, or an estimator which calls fit() and trains a model. There are many different options that you can include in your pipeline, including tokenizers, hashes, normalizers, etc. In terms of this dataset and for the sake of creating an easy to follow lab our pipeline started by using StringIndexer to turn the label (diagnosis) into a form that SparkML could use by encoding the input columns to a column of indices based on their frequency. Then a Vector Assembler combined the list of columns into a single vector column to be used in training the model. A normalizer was added to normalize each vector into a standard form to improve the algorithm. Lastly, our defined logistic regression instance was implemented and IndexToString was used to get the results of the model back into human readable form.
Following the definition of a pipeline, the logistic regression model and pipeline could be used to train and test the model. The data set was split with the standard 70/30 split for the training and test dataset, respectively. The training data set was then used to fit the pipeline and train the model to make predictions and the accuracy of the model was tested using a Receiver Operator Characteristic curve for binary classifiers. This value is calculated by plotting the true positive rate (recall/probability of detection) against the false positive rate (fall-out/probability of a false alarm) at various levels. A value when using the ROC curve that is close to 1 suggests that the model performs very well, whereas a value close to 0.5 is about as good as flipping a coin. Once the model was trained and evaluated, the test data set was used to make predictions The logistic regression model that was created in the steps previously described resulted in a value of 0.989, meaning it was able to predict the diagnoses of tumors very well.
Even though the model was already proven to be able to diagnose tumors accurately it could still be improved on. Hyperparameter tuning includes the use of model selection tools that test different parameter values for the pipeline and find the best possible values. There are two main options when working in Spark in terms of model selection tools, a CrossValidator or a Train-Validation Split. For this project we used a CrossValidator because even though they can be more expensive for larger data sets, they are more reliable when the data set isn’t sufficiently large because it evaluates each parameter k times, rather than just once.
CrossValidators first split the data set into “folds” which are used as separate training and test data set pairs. We set the value of the number of folds for this project to be 10 and therefore the CrossValidator generated 10 training/test data set pairs which are all used to test the parameters. The average performance among the 10 instances for each parameter are averaged and compared to other parameter values tested. We defined a paramGrid which stated the values to be used for the parameters within the pipeline. For this pipeline we could define values for maxIter, elasticNetParam, regParam, which are the parameters in the logistic regression model, or the normalizer parameter of the pipeline. Included in our paramGrid for this lab was parameter values for elasticNetParam, which must be between 0 and 1. This is an important parameter in the pipeline because it can make the model closer to a Lasso regression model (coefficients that are not relevant are set to 0) with a value close to 1 or a Ridge regression model (minimize the impact of irrelevant coefficients without setting them to 0) with a value close to 0. Because of this the values to test the elasticNetParam in the grid were set to 0, 0.5, and 1 to see which type of regression model would be best for the data. The second parameter defined in the paramGrid is the normalizer from the pipeline. The normalizer ensures that the algorithm runs correctly and the value set for the parameter represents the p-norm for normalization. The default value of 2 was previously used so within the paramGrid the values to be tested were set to 1 and 3.
After using a CrossValidator to find the best paramMaps, that model was trained using the testing data set and it was evaluated. The model improved 0.058% due to hyperparameter tuning, meaning the newly defined model was 99.5% accurate.
With an almost perfect predictive model defined, the last step was to grab the undiagnosed tumors from the original data set and use the model to predict their diagnoses with a high level of confidence.
The creation of this highly accurate model shows the power of Machine Learning in bettering the lives of people worldwide. It allows for the augmentation of breast cancer diagnosing and ensures that doctors see the patients in dire need of medical attention. Models such as these can help detect cancer earlier and, in more individuals, than doctors can do alone. Machine Learning has already started to be implemented in oncology to diagnose tumors, pathology to analyze bodily fluids, and in diagnosing rare diseases using facial recognition and deep learning to detect rare genetic diseases. Machine Learning serves many purposes from chatbots to augmenting the medical diagnostic process and with the continued advancements in technology and AI its applications are sure to expand even more.
I gave the keynote address to George Washington University’s DATA Conference on December 2. This is what I told the students. Please reply with your thoughts and ideas to extend the conversation on how to make the world a better place through data.
Think about how you can use data and data science to make the world a better place. We are now in a unique time in history because we now have huge amounts of data being collected by all the digitized systems in the world (almost 1 ZB or 1 times 10 to the 21st power Bytes) and the Data Science techniques are becoming more powerful and easier to use. These two factors will give you the ability to do more to improve the lives of your fellow students, their professions and society at large than has ever been possible before.
Data Science innovation will be central to solving humanity's grand challenges by capitalizing on this unprecedented quantity of data now being generated on human behavior and attitudes, human health, commerce, communications, migration and more. You can help to accelerate and advance the development and democratization of Data and Data Science solutions that can address specific global challenges related to poverty, hunger, health, education, the environment, and others.
To help stimulate your imagination, I will present several examples from our work at IBM. The key is to combine your growing expertise in Data Science, with your passions. At IBM, we are encouraging students to combine Data Science studies with other disciplines, such as natural science, social sciences, healthcare, etc. - - the problem domains where the Data Science can be put to work.
For the first example of “Doing Good”, I’d like to tell you about IBM Fellow Chieko Asakawa. She became blind at the age of 14, and as a result has devoted her professional life to building solutions to allow her and other blind people to access the world and regain their independence. Chieko has developed an object recognition solution so she can “see” ordinary objects in her home and at stores, and allow her to pick out wine or know the directions on a package – all using machine learning. She has also developed an indoor navigation system that helps her to easily get from place to place at work. Both use smartphones as the user interface. See these links for more details on Chieko’s inventions: Image rec: https://www.youtube.com/watch?v=RNp4OpToAdQ (many interesting solutions, Chieko’s is featured at minute 17); Nihonbashi Tokyo NavCon: https://www.youtube.com/watch?v=mlGcutE2t2A ; TED talk: http://www.ted.com/talks/chieko_asakawa_how_new_technology_helps_blind_people_explore_the_world ).
The second example is from IBM’s Cognitive Build Competition. Two IBM employees, Karibi and Jenn proposed and prototyped a solution to help children with Autism. The solution, dubbed Pino after Karibi’s newphew, uses Watson Conversation service to help children with autism communicate more independently by providing real-time verbal prompts. It can also be used with other conditions that affect communication ability, such as stroke and Alzheimer's disease. I met Jenn a few weeks ago. She told me, “At a birthday party a couple of years ago, I saw how upset my son was when he didn't receive a cupcake because he couldn't say "yes" when offered one. He needs a therapist or caregiver to prompt him to answer basic questions. He has a communication device that can help him speak, but it requires him to know he needs to respond… When Cognitive Build started, I thought it would be great if my son's communication device could be cognitive so it could help him to be more independent when I'm not around.” Learn more at this link: https://medium.com/cognitivebusiness/addressing-autism-project-pino-3741ce13d39
The third example is about the opioid epidemic, which has become one of the worst health crises in US history. In 2015, more than 90 Americans died every day from opioid overdoses, a number comparable to deaths in car accidents and projected to have risen further in 2016 and 2017. The Centers for Disease Control and Prevention (CDC) estimate the total economic burden of prescription opioid abuse to be $78.5 billion a year, including healthcare costs, lost productivity, and criminal justice involvement.
For many addicts, the problem often begins with legitimate healthcare treatment in which opioid painkillers are first prescribed, such as for surgeries or chronic back pain. During treatment, some patients become addicted and go on to suffer the well-documented consequences of addiction, while others do not, even if they become long-term users. To combat the epidemic, it is vital to understand the exact circumstances under which medically sanctioned treatments can devolve into addiction. That’s where data science comes in to play.
This summer, we took the first steps in tackling this question in a project within our Science for Social Good program. The team, led by Bhanu Vinzamuri, focused on analyzing the relationship between factors surrounding an initial opioid prescription and a subsequent diagnosis of addiction. We found that those people that received initial prescriptions for more than 7 days has a significant correlation to Long-Term usage, as does use of Synthetic Opioid prescriptions. We also confirmed that days of supply matters much more for addiction than quantity (e.g. in milligrams of morphine equivalent) prescribed per day. Other factors that were positively correlated with long term use and which should be used by doctors when prescribing opioids were age, certain regions of the country, rural location, healthcare utilization and depression, osteoarthritis, or diabetes. See more projects at http://www.research.ibm.com/science-for-social-good/#projects
Because of the power that Data Science and data is bringing to Humans, we need to be sure it is a force for good and not for evil. IBM and XPRIZE Foundation believes Artificial Intelligence (and the data science algorithms it uses) will be central to solving humanity's grand challenges. Solutions to pressing problems related to health and wellbeing, education, energy, environment, and other domains important to humanity can potentially be found by capitalizing on the unprecedented quantities of data and recent progress in emerging AI technologies. That’s why IBM is putting up $5 million for the Watson AI XPRIZE. See https://ai.xprize.org/ for more details.
But even if you are not up for competing for the AI XPRIZE, there is lots that you can do. Find a societal problem that you are passionate about. It all starts with a problem or need, like Chieko’s blindness, or Jenn’s child with autism, or the opioid crisis. Then come up with an idea or approach. There is a lot of data now available. Our Data Science Experience is out there on the web for you to play with. It is designed to allow data scientists, business analysts, stakeholders, and programmers work together on a data project. It’s easy to use. Go out and try it. There are tutorials to guide you. It is at https://datascience.ibm.com/ . Don’t just study the problem and write a school paper, create a solution that helps people. Your university’s office of entrepreneurship can help you to build a business case for your solution. Finally, consider pitching your idea to one of the many Pitchfests that are around. One I’m familiar with that exposes your ideas to corporate sponsors such as IBM is NCET2. They are at https://ncet2.org/. Go ahead and make the world a better place!
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Do you have Super Powers? Would you like to have Super Powers? I was recently invited to give a talk about AI at the Escape Velocity Science Fiction Conference (https://escapevelocity.events/) put on by the Museum of Science Fiction in Washington, DC. I focused the talk on how our advances in AI technology and augmenting human intelligence (Intelligence Augmentation = IA) are starting to provide humans with Super Powers, once only the realm of sci-fi writers. I’ve seen a lot technology come and go. And what we are now developing has the most potential of any of the technologies I’ve experience to help people to do more, do it faster, and do things we couldn’t do before. IA is going to have more impact on individuals, our professions, and society than all the previous advancements in computers to date.
John Campbell, the famous editor of Astounding Science Fiction, who published the likes of Asimov and Arthur C Clark, pushed his writers to create heroes and foes that had cognitive abilities that were better than humans, or had different attributes. So too, the comic books that came out featured heroes with unique powers, some cognitive and some around endurance and power. As you know, this vision of achieving super human capabilities has existed for most of recorded history. And it has been a dream of computer scientists for as long as computer scientists have existed too.
We haven’t made a lot of progress in the non-fiction world of creating people that are different - - Evolution is a VERY slow process. And while the world’s knowledge keeps increasing, people think pretty much at the same speed with the same memory limitations as before. Therefore, my talk focused on how we can use technology and data to help us to achieve super human capabilities.
Just like we’ve created assembly lines full of machines for our factories, we are starting to create tools to help those of us that are called Knowledge Workers to do our jobs more efficiently and create results that haven’t been possible in the past. Technology will redefine our professions and our jobs within our fields. These changes won’t just provide marginal improvements, they will provide significant new capabilities that will provide higher productivity to our employers, enhance our own well-being, and solve significant problems facing society.
We will know what customers are looking at in every store in the world, what they pass over and what they buy. Some might call that Omni-presence. We will be able to predict who will click on which ad on the web, who will buy which product, and who will get which diseases and which drugs will work for which individual. Is that Precognition or Clairvoyance? We’ll be able to instantly recognize a face in a crowd of thousands and see through objects. Our cars will help us to see black ice on the road and around corners. Our super-hearing will not only hear from a distance, but will allow detection of emotional stress and mental health issues that others might be facing – probably before they themselves realize it. Even better than superman!
In the government space, these super power of Super Vision will enable us to spot terrorists pictures among the millions of videos and images collected, as well as detect illegal fishing and logging operations. Precognition will allow us to predict the outbreak of a potential pandemic early enough to mount a robust public health defense, and predict weather events in time to evacuate and prepare emergency operations. In the cybersecurity world, we'll have to super power to detect threat patterns quickly, predict likely fast-fluxing techniques used by the intruders, and provide rapid advice for the response teams.
These Super powers come from taking all the data the world is now generating – which mostly is going to waste – and analyze it to find patterns and answers to questions that we couldn’t answer in the past. We’re now creating almost 10 Zettabytes per year – and the amount is increasing exponentially. Analyzing all that data will give us these superpowers and as that data grows, so too will our Super Powers. Analyzing all this data requires very sophisticated technology which IBM and others in the I/T industry are intensely developing. We will do this using Machine Learning, NLP processing, and reasoning.
My goal in the talk was not to talk about the technology but instead to show how far we have come in creating super human capabilities. I talked about some of the applications of IA – Intelligence Augmentation – to businesses, professions, and society. See the slides for some of the examples I used: https://www.slideshare.net/frankibm/getting-your-super-powers-with-watson-and-ia
I concluded with some discussion on how humans and machines can complement each other so that we can accomplish more together. It is my belief that we will need this collaboration to solve some of society’s hard problems such as climate change, supporting all the people that will soon be on planet earth, and even protecting us from incoming asteroids. The final slide shows 2 famous quotes regarding the value of the combination of people and machines:
- “The hope is that, in not too many years, human brains and computing machines will be coupled together very tightly, and that the resulting partnership will think as no human brain has ever thought and process data in a way not approached by the information-handling machines we know today.” - JCR Licklider, 1960, Professor at MIT
- “The computer is incredibly fast, accurate, and stupid. Man is unbelievably slow, inaccurate, and brilliant. The marriage of the two is a force beyond calculation.” – Leo Cherne, Presidential Advisor
Write to me at: firstname.lastname@example.org
The six years since IBM ushered in the new era of Cognitive Business have witnessed several pivotal transitions. The massive system of servers and disk drives that beat Jeopardy! using an advance orchestration of machine learning, natural language processing and statistical reasoning has evolved into a sophisticated set of services delivered through a world class cloud infrastructure. To help you understand the direction of these enhancements and their impact on Cognitive Business, the IBM Analytics Solution Center was pleased to have Rob High, IBM Fellow, VP and CTO Watson Solutions, present on the future of cognitive augmented intelligence.
Rob started by taking us back to the Jeopardy Challenge in 2011, reminding us how hard it is for a machine to answer a question correctly, but also how good people, like Ken Jennings, are at answering questions. What changed that allowed Watson to win at the game of Jeopardy? IBM took a different approach than classical AI which focused on semantics, ontologies, and rules -- IBM focused on linguistics and the use of machine learning to help uncover signals to the right answer.
Rob cited the consulting firm IDC’s Futurescape report that said that “by 2018, half of all consumers will regularly interact with services based on cognitive." Why this remarkable adoption of cognitive technologies? We collectively are generating so much data today that we can’t consume and make sense of all we are generating. Doctors can't read everything in their field – they would need to spend 150 hours a week to read everything, leaving no time for doing their job – or sleeping. Every one of us is in a similar situation.
What are Cognitive systems? Cognitive systems have 4 characteristics – they understand, reason, learn, and interact with people. Rob explained that these systems are different from traditional rule-based systems because they are taught based on data rather than programmed. This training data impacts how the system will answer questions – customers will use training data specific to their organization and thus create cognitive systems that conform to their organization’s business approach, and more broadly, its philosophy.
Since the Jeopardy Challenge, IBM has been very active in enhancing the technology and providing new Cognitive offerings. Rob focused on IBM’s latest work on Conversation services. Conversations are much broader than just answering fact-based questions. Conversations, whether between two people, or people and machines, should engage the user, understand the user’s concerns, build on an idea, and leave the user inspired and satisfied at the end of the conversation. In the best conversations, each party comes away from the conversation with new thoughts that were generated within the conversation. It will be hard to develop such a sophisticated Conversation service but this is our goal.
Rob then showed a video of a future Cognitive Mergers & Acquisition Advisor named Celia that responded to questions from two people analyzing acquisition targets. Celia could understand the conversation between the two people and then interrupted to ask, “It sounds like you are discussing the work we did last week, would you like me to bring up the results from that session?” Imagine a cognitive assistant that could participate in your conference calls, recalling previous action items, checking to see if the items had been accomplished, or performing analysis that it deems pertinent to the discussion.
One of the crowd-pleasers at the Seminar was the demo of “Embodied Cognition” using a Pepper humanoid robot (from Softbank Robotics) connected to Watson Conversation service. Besides answering questions, Pepper would turn to face the speaker, gesture with her (?) hands, and provide inflection in her voice. Pepper can also use the Watson Visual Recognition Service to recognize individuals and Watson Tone Analyzer to understand the user’s emotional state. Although the answers were no different than what Watson could provide without the Pepper embodiment, the human-like interactions were a strong draw to the humans attending the seminar!
Rob’s slides are available at www.ibm.com/ascdc under the May 31 event. Or email me if you’d like more information: email@example.com
My work this year has taken me from Big Data and Analytics towards Cognitive Computing and what IBM is now dubbing Cognitive Businesses (or Cognitive Government in our case). Cognitive businesses leverage cognitive computing technology (think Watson) to enhance, scale, and accelerate the expertise of their personnel. Below is the summary of the first part of a symposium I co-chaired last week. I'm happy to answer any questions you may have.
The AAAI Fall Symposia on November 12-14 included tracks on 1) AI for Human-Robot Interaction, Cognitive Assistance, Deceptive and Counter-Deceptive Machines, Embedded ML, Self Confidence in Autonomous Systems, and Sequential Decision Making for Intelligent Agents. This post will provide my general impressions of the Cognitive Assistance symposium.
Jerome Pesenti, IBM VP of Watson Core Development, provided the 1st day keynote. He started with the great quote from Fred Jelinek (Cornell/IBM/JHU) that “Every time I fire a linguist, the performance of the speech recognizer goes up.” He then talked about how deep learning is allowing reco systems that approach or surpass human performance. This led to a lively discussion with the audience on the universality of learning algorithms and whether the machines were learning in the same manner that humans learn something (no). Jerome finished with some applications of Watson including the Oncology Advisor, citizen support (e.g, tax questions), and security (finding relationships between data).
The rest of the morning was filled with examples of cognitive assistance for legal tasks such as filing a protective order (Karl Branting) and human-computer co-creativity in the classroom(Ashok Goel), and a tool to help SMEs define their vocabulary to find the most relevant content on the web (Elham Khabiri).
During lunch, much of the symposium had lunch together and a lively discussion ensued on cognitive assistance. One topic that I found interesting was on ultimate chess where human-machine teams compete. While these teams in the past have beaten computer-only teams, Murray Campbell noted that the advancements in chess playing computers are decreasing the value-add of humans to the team.
The afternoon session of Day 1 started with 2 interesting talks on cognitive assistance for helping those with cognitive disabilities. Madelaine Sayko described Cog-Aid which would include a cognitive assessment, recommender system (based on the assessment) and an intelligent task status manager for starters. Then Daniel Sontag described the Kognit technology program which includes tracking dementia patient’s behavior using eye tracking and mixed reality displays to assist the patient perform activities in daily living. Kevin Burns presented a sense-making approach that could be used by an intelligence analyst to help understand and define the Prior and Posterior probability calculations as new evidence is added. This could eventually be embodied into a cognitive assistant. Next came a presentation on capturing cybersecurity operational patterns to facilitate knowledge chaining by Keith Willett.
The final session of the day was a panel discussion of workforce issues associated with cognitive assistants led by Murray Campbell. Erin Burke of Fordham University Law School talked about how legal education must transition and that she is working at the intersection of law, big data, and cognitive computing. Jim Spohrer, Director of IBM’s University Programs, provided some predictions including that by 2035 everyone will be a manager and will have at least one Cognitive Assistant working for them. A lively discussion ensued with the audience about our forthcoming relationship with Cogs including whether we could trust them, unintended consequences, whether we can build common sense into a Cog, and whether our brains will atrophy as we depend on Cogs.
I’ll cover Day 2 in the next blog post.
In medieval times, Alchemists hoped to convert base metals
into the noble metal gold through the use of a Philosopher's Stone.
Today, in the field of information science, we talk about
Information Alchemy, converting data into information and then into
knowledge. Some people even add a 4th
stage of converting knowledge into wisdom[i], but
that will be for another blog post.
Data is defined as the raw characters or numbers, whereas information is
defined as the processing of that data into various relationships so they have
some meaning. Dr. Eisenberg at the University of Washington describes knowledge as the
“collected, combined, organized, processed information for a purpose.” Over time, it is thought that accumulated and
refined knowledge leads to Wisdom.
This year, the total of all digital data created is forecast
to reach close to 4 Zettabyes, or 4x 1021, according to IDC[ii]. This is nearly four times the 2010 volume and
it is growing rapidly. All of this data
should let us make a smarter and better planet.
However, today we’re drowning in all this data because we don’t have the
time as individuals to process all this information, and we don’t have computer
systems that can turn this data into insight,
But soon that will change.
We are entering a new era in computing which IBM is calling Cognitive
Computing. The first of these systems is
the IBM Watson system which debuted on the Jeopardy! Show 2 years ago. Traditional computing systems have done a
great job with handling data, including storing it and manipulating it into
information. So now we have lots of
financial, inventory, customer, and all sorts of other, mostly numerical,
We also have lots of unstructured information such as text,
audio, graphics, and video. We used to say that 80% of the new bytes being
created today were associated with unstructured data, but that number is
probably closer to 90% given all the video being created these days. This text and multimedia information is
human-readable – in fact, it is designed by humans for humans to understand but
is not easily understandable by today’s computers.
And that is a considerable problem. Today, the transformation of information into
knowledge is primarily done in people’s heads.
Not just by scientists, engineers, or financial analysts, but by
everyone who reads an article or watches a video. The time available for people (some would
say skilled people) to analyze information to gain insights (knowledge) is the
limiting factor in the production of new knowledge today. To say this another way, we are now
information-rich, but knowledge-poor.
The goal of the cognitive computing efforts is to remove
this limitation by designing computer systems that can take this abundance of
information, much of it in human readable/viewable formats, and convert into
knowledge. For example, in the Jeopardy!
IBM Challenge, the Watson computer system analyzed its deep information stores
to find the answer that best answered the clue and the category. It did this feat by utilizing many different
algorithms to attempt to “understand” the text information and a machine
learning (artificial intelligence) scoring system to select the best response.
In a more significant effort, IBM is working with Memorial
Sloan-Kettering and WellPoint (a major BC/BS licensee) to use cognitive
computing technology to assist doctors by helping to identify individualized
treatment options for patients with cancer. It is, in effect, creating knowledge of the
appropriate treatment options from information about the patient’s condition
and medical history, and information from clinical trials and best practices on
While the field of cognitive computing is just beginning, I believe
over the next several years, we will learn how to perform “Information Alchemy”
and we’ll see how this newly created knowledge can benefit our organizations
and our lives.
As the quintessential information-based organization, government agencies may be in the biggest need for "information Alchemy." Do you seen this need? Do you see opportunities for Cognitive Computing at your agency?
Director of IBM’s Analytics
[i] Eisenberg, Mike,
“Information Alchemy: Transforming Data and Information into Knowledge and
Wisdom”, March 30, 2012, http://faculty.washington.edu/mbe/Eisenberg_Intro_to_Information%20Alchemy.pdf
Watson is the only computer on the planet that can answer a Jeopardy!
question in less than three seconds - fast enough to be competitive with the
world’s best human players.
of you that missed the match click here
to see a video clip from the match.)
But can a Watson-like computer help the government?
Watson was optimized to tackle a specific challenge:
competing against the world’s best Jeopardy! contestants. It does this by sifting through large amounts of unstructured information to find potential answers and assigning a confidence measure to each potential answer. When it has high confidence in an answer, it will buzz in and offer the answer. Beyond Jeopardy!,
IBM is working to deploy this technology
to businesses and governments dealing with the information overload
problem. At work, few of us are like
Ken Jennings, able to instantly answer almost every question thrown at us - -
with an 80-90% success rate. There is
simply too much information and more information is coming in all the
time. Whether we’re in finance, HR, IT,
or another area, our success at work depends upon dealing with huge volumes of
information, sifting through it to find
the “good information”, and then using the information to make decisions to do our
job. Technology like that used in Watson can provide for our consideration potential answers as well as the "evidence" it used to come up with potential answers.
In discussions recently with some of our military colleagues,
they came up with numerous ideas for deploying Watson-like technology. They cited the problem of “request overload” - - dealing with all the
requests for Predator and similar UAV missions.
How could they deploy their limited resources to best effect? Another person mentioned the problem of
sifting through all the intelligence information – most of it in the form of
unstructured information formats such as video and text – to find the relevant
information to a mission they were planning.
Another discussed the problem of monitoring their “situational
awareness” and how hard it was to keep track of all the data coming in. “Could Watson help monitor our security
posture and alert us to potential threats?” asked another.
Are you dealing with massive amounts of information? How could a Watson-like system assist you at
work? Do you want to recruit Watson to
work for your agency? We want to hear
your thoughts either in this blog or directly.
Write to me at firstname.lastname@example.org.
We’re hosting 2 free Watson Overview Briefings
on July 26 and 27. More
information at our website: www.ibm.com/ascdc
Frank Stein, Director, Analytics Solution Center