Watson Weighs in on the U.S. Presidential Race


Hillary-Trump-DaveWhat does the world’s most powerful artificial intelligence say about the U.S. Presidential Candidates?

I spent the last week engaging with the world’s smartest artificial intelligence brainiac about the U.S. Presidential Election. I pressed IBM’s Watson to light up its neural networks, apply cognitive systems, crunch semantic learning, and emote sentimentalism to help me understand what the candidates are saying and meaning.  The results were astonishing.

IBM’s Watson platform is a cognitive system enabling a new partnership between people and computers. It is most famous for beating the Jeopardy champions for a $1 million prize.  The Watson platform has introduced groundbreaking advancements in healthcare, finance, biotechnology, and other sciences, and is entering the fields of education and finance. With that breadth of intelligence, a natural question to ask Watson would be, “What do you think of the U.S. presidential race?”

Mr. Watson. Come Here. I Want to See You

As a cloud-based computer platform, Watson should be an apolitical judge.  Watson uses natural language processing to understand meaning in text messages. Coyly, one may argue there is nothing natural about this presidential election. Nevertheless, Watson is the best artificial mind we have to interpret what the candidates are saying and what they actually mean.

When engaging Watson, I didn’t explain the context of everything related to the election, not Donald’s business successes/failures, nor Hillary’s emails. Why bother? Watson is in the “cloud.” It lives in the Internet even more than I do.

To prime Watson for our conversation, I provided transcripts from acceptance speeches at each party’s national conventions and ran them through IBM’s Alchemy Language – Watson’s brain for text analysis using natural language processing. Watson’s Alchemy Language engine analyzes text to help understand concepts, things, keywords, sentiment, and emotions.

Determining context from a phrase is not an easy task neither for humans with political candidates, nor for Watson trying to dissect and understand a political speech.  For example, in Hillary Clinton’s speech she said, “I will be a President for Democrats, Republicans, and Independents.” Is this neutral, positive, or negative? For Republicans and Independents, maybe negative. Unless you’re a republican who loathes the idea of a President Trump. The beauty is in the eye of the beholder.

When Donald Trump said, “The problems we face now – poverty and violence at home, war and destruction abroad – will last only as long as we continue relying on the same politicians who created them,” his words contained problems, poverty, violence, war, destruction – four highly negative words. However, anyone reading the line may interpret it as a call for change, hope, and a better future.

To best illustrate the problem of attaching sentiment and emotion is the line from Donald Trump’s speech when discussing the horrific Orlando nightclub massacre, “49 wonderful Americans were savagely murdered by an Islamic terrorist.” The word wonderful is a positive emotion attached to Americans. Savagely, murdered, and terrorist are all negative words also attached to the Americans, Islam, and terrorists. Donald Trump was not being negative towards Americans, but the semantics of the words associated with their objects was negative. These are the complex problems cognitive systems must understand to be most beneficial to humans.

Screen Shot 2016-08-24 at 4.48.53 PM

Donald Trump’s Acceptance Speech Word Cloud

That’s Good, But How Do You “Feel?”

When Watson extracted and examined the top 50 things (entities) that each candidate mentioned in their speeches, it found that Donald Trump was positive only 22% of the time, compared to 38% for Hillary Clinton. Both candidates were negative about 35% of the time. Donald Trump’s primary sentiment towards things was neutral, while Hillary Clinton’s was positive.

When Watson extracted and examined the top 50 keyword concepts from each candidate’s speech, Watson found that Donald Trump was positive only 22% of the time versus 58% for Hillary Clinton. Inversely, Trump was negative 66% of the time compared to 30% for Hillary.

For targeted sentiments, Donald Trump was negative 65% of the time, while Hillary Clinton was positive 65% of the time. Donald was positive only 25% of the time, while Hillary was negative only 30% of the time.

Eliminate All Other Factors, and the One Which Remains Must Be the Truth

There were many areas where Watson hit the bullseye.  Below are some top examples:

  1. Each candidate’s number one topic was America
  2. Each candidate’s number two topic was their opponent, and they were negative about their opponent.
  3. Donald Trump speech spoke often about countries in the Middle East and was negative about these countries.
  4. Both candidates spoke positively about their family members,
  5. Hillary Clinton was positive about “young people,” “small businesses,” “good paying jobs,” and “better lives.”
  6. Donald Trump was positive about the “American People,” “United States,” “new wealth,” and a “truly great mother.”

The Empires of the Future are the Empires of the Mind

What were the core topics covered in each speech? According to Watson, Trumps speech mostly covered societal unrest and war, and children. Watson wasn’t confident that Trump’s speech was about government. Perhaps Watson has an ironic point. Clinton’s speech, by contrast was about foreign policy, war and unrest, and education. Watson was confident about all three topics for Hillary’s speech.

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Hillary Clinton’s Acceptance Speech Word Cloud

We’re All Quite Mad Here! You’ll Fit Right In

When Watson examined the top 5 concepts relating to each speech, here’s what it found:

Donald Trump’s top 5 concepts in his Acceptance Speech:

  1. Hillary Clinton
  2. Bill Clinton
  3. Barack Obama
  4. Illegal Immigration
  5. United States

Hillary’s Clinton’s Top 5 concepts in her Acceptance Speech:

  1. Joe Biden
  2. United States
  3. Barack Obama
  4. President of the United States
  5. Donald Trump

Wow! I have watched, listened, and read Hillary’s speech several times now, and I cannot understand how the top concept Watson identified was Joe Biden. Above the grand old USA. That’s a big miss.  Also surprising is that Bill Clinton was #2 on Donald Trump’s list.

Come On, Group Hug! You Too, Anger.

Watson examines the same 5 emotions as the Pixar animated film, Inside Out: Fear, Joy, Disgust, Anger, and Sadness. While humans have many more emotions, both Pixar and Watson limit their evaluation to these five. Watson found the emotions presented in the acceptance speeches both conveyed Anger and Fear. The other three emotions (Sadness, Disgust, and Joy) didn’t rise high enough to be considered significant. Sadly, Watson disgustingly didn’t find either speech to be joyful.

No Great Mind has Ever Existed Without a Touch of Madness

We are at the late dawn of cognitive systems, and by using Watson to interpret the presidential candidates’ acceptance speeches we are giving it an arduous task that exposes how well it works as well as areas requiring advancements in interpretation both for the computer, for the analyst, and the reader. For the list below, I examined Watson’s interpretation, then re-read the speeches to try to understand what Watson was thinking and if it was correct or erroneous.

Watson’s biggest misses were:

  1. Hillary Clinton’s sentiment about America was negative.
  2. Hillary Clinton was negative about Bernie Sanders.
  3. Hillary Clinton was negative about the President and vice president.
  4. Donald Trump was neutral about President Obama.
  5. Donald Trump was negative about Americans, country, and America.
  6. Donald Trump was negative about law enforcement officials.
  7. Donald Trump was neutral about African American youth.
  8. Hillary Clinton was positive about Trump picture frames.
  9. Hillary Clinton was negative about police officers.
  10. Hillary Clinton’s top concept was Joe Biden.
  11. Donald Trump was neutral about Fred Trump
  12. Hillary Clinton was neutral about Bill Clinton.
  13. Hillary Clinton was negative about First Lady Michelle Obama.
  14. Hillary spoke negatively about Joe Biden
  15. Hillary Clinton was negative about “real change.”
  16. Donald Trump was positive about “Special interests.”
  17. Donald Trump’s 2nd most relevant concept was Bill Clinton.

When Gunpoint is a Country

Watson assigns entities to a category, and some of these categories were incorrect. Watson assigned the White House to be an Organization in Donald Trump’s speech, but a Facility in Hillary Clinton’s speech. In Donald Trump’s Speech, Watson found America to be a continent, and Gunpoint to be a country. In Hillary Clinton’s Speech, Watson misidentified New York state as a city even though the sentence where it was mentioned said, “the great state of New York.” Watson also identified the Secretary of State as a “Field Terminology” rather than a JobTitle. (It understood President to be a JobTitle.)

Note: FieldTerminology appears to be the category when Watson knows an entity is an unnamed thing.

Watson, the Case is a Foot

Understanding meaning isn’t as easy as we humans give it credit for. Computer scientists will tell you that the most valid results come from providing enormous data sets for a cognitive system to learn from (big data analytics), and then allow it to identify patterns that develop over time. Giving a single speech is likely to provide atypical results. Since this is an atypical U.S. presidential election, it seemed appropriate to ask Watson to weigh in.

Cognitive systems are an emerging technology. I’ve worked with semantic matching engines for over a decade and know much about their horrific abilities to misinterpret and misunderstand. It’s easy to poke fun at them and find their deficiencies relative to human understanding. However, it’s even more amazing to see what they can do and what they do get right.  IBM is aligning their business to the rise of cognitive systems, and it’s making a difference. IBM Watson Healthcare is helping improve healthcare performance at a local and global level. IBM Watson is beginning to focus on helping improve education, and is making inroads into banking.

Elementary, Dear Watson

After all Watson’s analysis of the candidate’s acceptance speeches, where does it leave me? Where does it leave you? Us?

Watson conclusively found Donald Trump’s speech to be significantly more negative than Hillary Clinton’s.  It did an amazing job of correctly extracting and interpreting much of the speeches text with the whimsical, endearing, and head-scratching exceptions noted above. Overall, Watson leads us to the two questions we really want the answer to: “Who will win?” and “What will that mean?”

 


Data Summaries

The tables below are directly taken from Watson’s search results. Notes are indicated to help address any confusion and anomalies.Watson Presidential Test - Entities

Watson Presidential Test - Keywords 

Watson Presidential Test - Targeted Segment

Watson Presidential Test - Concepts

Watson Presidential Test - Taxonomy

 

Watson Presidential Test - Emotion

Notes

beofpp-14

Are we at the cusp of actually achieving a comprehensive education-to-employment cloud?


The Institute for Higher Education Policy has published a fantastic infographic mapping a rubric’s cube of the relationships between workforce, post-secondary, and  employment data. This provides unparalleled opportunities for innovation, insights, and deep analytics to significantly improve the education-to-employment and pathways to prosperity systems.

As many of you know, I’ve long advocated for advancements in the systems to assess the unified data.  IBM’s Watson cognitive system approach offers an immense opportunity to significantly improve the system. Their integration of big data, analytics, artificial intelligence, semantic learning, audio and text analysis and transformation, emotional intelligence, and visual learning coupled with big data analytics has already shown extreme value in advancing healthcare. IBM just announced their launch of Watson Education to apply the same platform to the field of higher education and vocational training. They are working with Pearson on developing a system, and have several outlier partners working on trying to change the game. The same technologies can, and will one day, be applied to workforce and education-to-employment. Anyone who wants to learn more or undertake a project to apply Watson to big data, give me a call at (984) 212-2285 or email at demaionewton@hotmail.com

In partnership with the Workforce Data Quality Campaign (WDQC) and the State Higher Education Executive Officers Association (SHEEO)PostsecData, an initiative of the Institute for Higher Education Policy, released an infographicMapping Postsecondary and Workforce Information Gaps in State Data Systems, that explores the data that exists in and is missing from State Longitudinal Data Systems (SLDS).

SLDS match data from different state sources about individuals over time and serve as an important resource for state policymakers, researchers, and the public. While there are challenges to filling in the information gaps that exist in these systems, stakeholders need these privacy-protected data to understand how individuals progress through K-12, postsecondary education, training, social service programs, and the workforce to help people access credentials, employment, and higher earnings in the states.

Click here to learn more about the data that do and do not exist in SLDS and learn more about the Institute for Higher Education Policy.

Click here to view the infographic

About the Institute for Higher Education Policy

The Institute for Higher Education Policy (IHEP) is an independent, nonprofit organization that is dedicated to increasing access and success in postsecondary education around the world. Established in 1993, the Washington, D.C.-based organization uses unique research and innovative programs to inform key decision makers who shape public policy and support economic and social development. IHEP’s web site, www.ihep.org, features an expansive collection of higher education information available free of charge and provides access to some of the most respected professionals in the fields of public policy and research.

monster-linkedinw

7 Results and Recommendations from Microsoft’s Acquisition of LinkedIn


In 2005, Monster Worldwide was riding high as the champion of the online recruiting world.  They had a healthy lead over their #1 rival, CareerBuilder, and #2 Hotjobs. And they were looking to cement their lead through acquisition of other recruitment companies competitors. The question was raised whether they should enter the staffing business, but the answer was consistently, “No,” because the staffing industry required people and didn’t have the same return-on-investment as the lucrative job posting/resume search/recruitment advertising business.

As Monster’s Senior Director of Workforce Solutions Strategy, I had been watching this small company named LinkedIn and saw how they were making a value play, but not in the way you might first suspect. Their creation of a widget that could be installed on a person’s browser to see who they knew who worked at a company whose job they were viewing on Monster or CareerBuilder was sheer genius. That it didn’t require an agreement with Monster or CareerBuilder was brilliant hijacking.

The second value play I saw was their foresight into the impact of the relationship age. The fact that they were connecting people with people they knew was powerful.  A few years earlier, Monster had hired a leader from Match.com to attempt the same thing, only to see it fail gloriously to the tune of millions of dollars.

There was a team put together to examine the company and make a recommendation to the executive team. The recommendation was a unanimous “Buy.” At the time, LinkedIn could be purchased for the tens of millions of dollars. The recommendation was made, and Bill Pasteur, Monster’s CFO at the time rejected it. He “didn’t understand how they’d make money.” Instead, he focused on using that money to go on a sales hiring blitz for telesales to drive short-term revenues over long-term value.

Later, I would come to refer to this as my “Billion Dollar Recommendation” that Monster rejected.  How wrong I was when Microsoft announced the acquisition of LinkedIn today for $26B.  Now it’s my $26B recommendation that Monster ignored. (Side note: the price Microsoft paid for LinkedIn equals the salary for 54,126 North Carolina teachers for one year).

Below are Seven Results and Recommendations from Microsoft’s Acquisition of LinkedIn:

  1. Monster Worldwide should sell to IBM.
    Not Yahoo. I struggled to get Monster to be a value creator and to become The Career Company. The executive board fought (and largely won (lost?) by making it the Yahoo! of job boards). I don’t believe that Monster’s semantic matching is better than boolean. I do believe, however, that IBM’s Watson cognitive systems would have a major impact on Monster’s competitiveness.
  2. CareerBuilder should form a strategic partnership with Google, Apple, or IBM
    CareerBuilder is the market leader in the traditional recruitment space. Their focus on expanding value in the talent supply chain for recruiters and job seekers is impressive. While they can continue on that path alone, their best play is to form a strategic partnership or to be acquired. My own feeling is that a partnership is a better move.
  3. LinkedIn just got deep pockets. Microsoft can use LinkedIn’s matching with their own semantic engine to further expand their market leadership.
  4. The move benefits LinkedIn.
    LinkedIn had plateaued. My conversations with ex-LinkedIn employees and own experience was that the company was focused on building a sales and marketing machine similar to Monster’s, and had shifted from a value play to a market leader defensive strategy (Wrong move).LinkedIn’s access to Microsoft’s technologies, R&D, Skype, Cloud platform, semantic matching engine, and services provide significant opportunities for LinkedIn to create greater value and capture greater market share.Not surprisingly, their most recent evaluation of top investment research firms recommendations to investors as follows

    •  1 Buy
    • 4 Neutral
    • 2 Underperform
    • 3 Sells
  5. The move benefited Microsoft
    Microsoft has been at a crossroads lately, having taken bruises from its phone platform, music platform, and Office/Windows challenges. However, their development, server platform, services, acquisition of Skype and launch of Microsoft Cloud platform are well positioned for success. The acquisition makes them the market leader in recruitment and the integration with their advertising channels and semantic engine should cement their ability to increase LinkedIn’s leadership in the recruitment industry.
  6. The acquisition weakens Monster’s technology platform
    While I’m certain Microsoft will attempt to ensure fairness of access to their application development, server, and other resources, the fact that LinkedIn will have access to Microsoft’s R&D centers, experts within arms length, and Microsoft’s market channels puts further stress on an already stressed Monster.
  7. Monster needs to create new value quickly
    The challenge to Monster is to create a true value play to disrupt the market. To do this they need to adopt a different view and focus on penetrating the talent supply chain quickly, uniquely, and in a way that inspires investors and customers. The acquisition of Jobr and their mobile app is good as it improves Monster’s mobile capabilities, but doesn’t expand the value proposition. All moves will be very challenging and under heavy scrutiny given Monster’s low stock price ($2.78/share). Monster’s most recent evaluation by top investment research firms is:

    • 1 Buy
    • 1 Outperform
    • 8 Neutral
    • 1 Sell

 

 

 

 

Opportunity Universe

The 30-Day Job Challenge: Day 11


To all my well-wishers and Schadenfreudian colleagues, I wanted to thank you for your patience in giving the latest on my 30-Day Job Challenge.

The good news is that there are several opportunities in the pipeline, including interviews with The College Board, Bounce USA, Helix Education, Dreambox Learning, and Instructure. I had an interview yesterday with Helix Education, have one today with The College Board, and two interviews tomorrow with Dreambox Learning and Instructure. All great companies doing great things to improve education.

Today I had the great fortune to talk with Kyle Helfrich, a colleague from Houghton Mifflin Harcourt. Kyle is one of the leaders who contributed to turning that company from bankruptcy to publicly-traded. He’s now at Springboard, a College Board subsidiary. He’s loving the role, and I am very happy to see him happy. There are people who bring you energy and people who drain your energy. Kyle is the first kind. He is authentic, brilliant, committed to making a difference, and incredibly adaptable to what is needed. When I listen to him, I get excited about his ideas, work, and possibilities.

The bad news, as you’ve probably noticed, is that the 30-days have passed. I have failed. It certainly won’t be the last time, that’s for sure. Instead, I’m redefining 30-Days. I will post updates for the extended 30-Day Challenge, but I won’t post daily, that’s too much.

While aerating my lawn earlier this week, I met a woman, Ginny – a grandmother from Pittsburgh, who was visiting her son, daughter-in-law, and caring for the two young boys. Ginny is 62 and worked in healthcare processing for most of her career. She had been laid off from her job and started collecting social security – even though she really wants to work. She figures, “Who’s going to hire me?” She told me she really wants to work part-time.

I referred her to Indeed.com and RetirementJobs.com. I asked if she has a resume, and she withdrew a little, explaining that it’d been 15 years since she’d last looked for work. She had one somewhere, or a part of something…

Ginny is one example of the great need to better connect people with jobs. The system is largely broken – relying on the individual too heavily to find the opportunity. We can and must do better. It affects us all exponentially.

Ginny is a wonderful and kind person. We talked about art and painting. Here was an opportunity that neither of us probably would have had. We both made a friend.

I’ve been working on the development of the National Center for Social Impact. I’ll tell you more as it develops, but it’s been a project I’ve been setting up for several years. The focus is to bring together leaders and organizations contributing to making a positive social impact. Innovators, Entrepreneurs, Community Leaders, and organizations will learn new ways to address challenges, improve their performance, and make a stronger and larger impact. But that story is for another blog.

As for now, I still need your help. Send me hidden jobs you know about. John Cohn was great in connecting me to the head recruiter for IBM Watson Education. They’re doing really cool stuff. Fingers crossed it will lead to something. Cognitive Systems applied to Education can have a profound impact if used correctly. I shudder to think about the unintended consequences of misuse.

Thank you to all who have reached out. It’s great reconnecting with many of you. And thank you to those well-wishers for your kind thoughts as well. Your support means the world to me.

day-10

The 30-Day Job Challenge: Fighting for Progress


 

Creating My Future

IMG_9631I had a wonderful call with John Cohn, who’s an IBM Fellow working on the Internet of Things.  John, as you may recall, is a good friend of my neighbor, Salter Smith – who introduced us. He mentioned that his boss was helping to create a business unit for Watson Education.  Watson, as you may recall, is the artificial intelligence engine that won Jeopardy.

Unknown-2The opportunity to apply the intelligence to education could have incredible or disastrous results depending on the intent and approach. Imagine if student learning centered upon holistic education to help students develop emotional intelligence, to be better human beings, and to achieve their potential.  Want to be inspired?  Watch Big Blue Meets Big Bird.

John asked what he could do to help. I answered 3 things:

  1. Let me know if he hears of opportunities with IBM that might be of interest to me.
  2. Serve as a referral within IBM.
  3. Come visit Slater & Suzy Smith so we can hang out together

Following our call, I tracked down the contact information for his boss and reached out to her via email at 3:30AM EST.  She’s based in the UK, so it shouldn’t have come as a surprise when I got a message back at 4AM EST. She was very kind and asked another on her team to reach out and see what the needs are and how I might fit.  Fantastic.  I’d love to help transform education in a powerful and global way.

The 30-Day Job Challenge Campaign Results

As I mentioned earlier, my email blast was at least helpful in seeing who’s not where they were when I last was in touch with them.  As a result, my contacts went from 6,200 to only 3,761.  My email blast asking for you to participate in my 30-Day Job Challenge was sent to over 1,600 people I know. I used 3 different platforms to see which was most effective. Continue reading