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Monday, March 13, 2017

Stop the AI BS already


I am getting really tired of all the AI hype. Last week alone, the Wall Street Journal, Forbes, Fox News, Huffington Post, MIT Tech Review, The Guardian,TechCrunch, Bloomsburg, Newsweek, Fortune, Fast Company. and a host of lesser known publications had articles hyping AI. In contrast, Atlantic Magazine actually had a reasonable article about why the term AI has become meaningless.


So, I thought I would take a moment to explain something simple about AI. Most of my work in AI was an attempt to get computers to understand English. We had a program hooked up to the UPI wire at one point that could summarize a story, answer questions about the story, and translate that story, To do this we had had to carefully represent  various domains of knowledge. So if we wanted the program to understand stories about diplomatic visits for example, we had to represent in gory detail what took place on a diplomatic visit, why that visit took place, and what kinds of accomplishments were hoped for and might be achieved.

To help you understand how hard this is. I wrote down some words that I saw in today’s New York Times:

restraint
stimulate
sustainable
protest
turbocharged
prosecutor
quintessential
hallmark
bid-rigging
cyber-criminal
full-fledged
devolved
concessions
crackdown
discrimination
movement
communitarian
faith
self-deprecation
filibuster

An “AI” that read stories or did anything else would have to understand what these words meant. Many people wouldn’t be able to explain them all. But, today, we are told about “AI’s” that can deal with the words it finds on the internet in various ways and then we must all watch out before they take away our jobs.

I know this is not true because I know how hard it is to represent the complex meanings of words like this, and I know that the “AI” that is being worked on now isn’t even trying to comprehend these words. Todays “AI” is all about counting words and finding superficial patterns among them. No matter how many times you count the word discrimination you would not comprehend what it was about, why it might matter, nor would you understand which sense of discrimination was being used when you read it.

What does communitarian mean? I can guess and can figure it out in context. Current “AI’s” can count it. What does quintessential mean? Could you explain it to a computer? How about self-deprecation? Try explaining that word to a child. AI needs to do simple things like figure out what a word might mean and explain what it has just read to others. We are nowhere near doing that.

Let me try to explain just one of these words. Let’s look at “faith.” What does it mean to have faith in someone? It means we believe that they will do what they say. Or it could mean that we believe they will do their best to come through in a difficult situation. But faith refer to more than people. You could have faith in a company, which would mean that you believe their products are good. Or you could have faith in the system which means that you think you should follow the rules. Or you couch have faith in a religion which means you believe their teachings. Faith also connotes a kind of optimism. But there is also the word faithful which in the context of religion is the same as faith but in the context of marriage has to do with extra marital affairs.

How do we explain this to a computer? To do that we need to detail the rules of marriage or work (a “faithful employee.”) You could be a faithful advocate of a political persuasion or religion or point of view on life. But for a computer to understand all this it would need to comprehend political philosophies, religious philosophies and a whole lot more. You could a faithful follower of a band or you could be playing Minecraft which has a faithful resource pack.

My point is this: AI requires modeling the world in gory metal so that we can comprehend people's actions, intents, beliefs, and a while lot more. Sorry, but matching keywords is not AI.


But the press will keep on telling us how an AI will suddenly take our jobs and how chat bots are the answer to customer service. I don’t know about you but if I got a chat bot answering my customer service call, I would hang up.  Or maybe I would filibuster. Or maybe I would show some restraint. Either way no AI would know what I was doing nor would it understand if I explained it.

Tuesday, March 7, 2017

The SPGU Tool: A response to current so-called AI



OK. We have to fight back. Enough with the AI is going to take over the world stories. Enough with chat bots. Enough with pretending AI is easy. Enough with AI people who barely know the first thing about AI.

I am not discussing machine learning here. If you want to count a lot of words fast, and you can draw some useful conclusions from that, go ahead. I wish you wouldn’t call it AI, but I can’t control that. But I can fight back. Not with words, which I know don’t really convince anyone of anything, but with a new AI tool, one that uses what I know about AI, in other words, what most people  who worked in AI in the 60’s, 70’s, and 80’s likely know about AI.

The SPGU Tool is named after the iconic book by Schank and Abelson (1977) Scripts, Plans, Goals, and Understanding.

In that book, we laid out the basis of human understanding of language by invoking a set of scripts, plans, goals, and themes, that underlie all human actions. This was used to explain how people understand language. The classic example was the attempt to understand something like “John went into a restaurant. How ordered lobster. He paid the check and left.” This understanding was demonstrated by the computer being able to answer questions such as: What did John eat? Who did he pay? Why did he pay her? In this easy example of AI, SAM (the Script Applier Mechanism we built in 1975) could answer most questions by referring to the scripts it knew about and parsing the   questions in relationship to those scripts. In this example, given a detailed restaurant script, it could place any new information within that script and make inferences about what else might be true or what might possibly be asked at that point in the script.

The SPGU Tool (SPGU-T) takes that 1970’s technology and makes it useful in the modern era. People who plan often need help in making their plans succeed. A tool that helps them plan needs to have a detailed representation of the context of that plan, what goals were being satisfied and well-known obstacles to achieving those goals. Then it can access expert knowledge to assist a planner when the planner is stuck. We used this methodology when we built the Air Campaign Planner for the Department of Defense (in the 90’s). We captured expert knowledge (in the form of short video stories), tracked what the planner was doing within a structured air campaign planning tool, and offered help (in the form of one or more retrieved stories) when SPGU-T saw that help was needed.

In a project for a pharmaceutical company, for example, one expert story we captured was called the “Happy Dog Story.” The story was how the company had found a drug that made dogs every happy and then went into clinical trials with humans very quickly. Some months later, the dogs had all killed each other, but the people who were doing the clinical trials were unaware of this. This story should come up when a planner is planning clinical trials and is relying on data that required continued tracking. SPGU-T would know this and be able to help, if and only if, all of the planning for the trials was done within SPGU-T’s framework that detailed the steps in the clinical trials script.

A partner or manager in a consulting firm could use SPGU-T to plan a client engagement. SPGU would be able to help with problems and suggest next steps at each stage if it knew the gory details of how engagements work, and if it had stories from experts addressing well-known problems that occur in engagements. SPGU-T could not only answer questions, but it could also anticipate problems, serving as a helpful expert who was always looking over the user’s shoulder.

A Deeper Look at SPGU-T

It is well beyond the state of the art, both now and in the foreseeable future, for a computer system to answer arbitrary questions, or more difficult still, to deeply understand what a person is doing and to proactively offer advice. Both of these forms of intelligent assistance are possible today, if the person is working to accomplish a well-defined, goal-oriented task using a computer-based tool that structures his or her work. In other words, if we can lay out the underlying script, and we can gather used advice that might be needed at any point in the script, we can understand questions that might be asked or assist when problems occur. That understanding would help us parse the questions and retrieve a video story as advice in response.

This isn’t simple but neither is it impossible. Advisory stories must be gathered and detailed scripts must be written. We built the needed parser years ago (called D-MAP for direct memory access parsing.)

SPGU-T helps someone to carry out a plan in a specific domain, be it planning a large-scale data analytics project, a strategy consulting engagement, a construction project, or a military air campaign. It does so by knowing a person’s goals in creating such a plan, the steps involved in plan creation, the nature of a complete and reasonable plan, and the problems that are likely to arise in the planning process.

Imagine, for example, a version of SPGU-T that is customized for developing and tracking a project plan that a consulting firm will use to successfully complete a complex data analytics project. It knows that its registered user is an engagement manager. Given the usage context, it also knows that the user’s goal is to plan a time-constrained, fee-based project on behalf of a new client. From this starting point, SPGU-T can take him or her through a systematic process for achieving that goal. At any step in the process, SPGU-T will know specifically what the user is trying to accomplish and the nature of the information he or she is expected to add to the plan. For example, in one step, the user will identify datasets required for the project. SPGU-T will expect him or her to identify the owners of those datasets, the likely lag times between data requests and receiving the required data, and any key properties of the data, such as its format and likely quality.

This very specific task context, computer-based interpretation of the semantics of the information being entered, and heuristics to infer reasonable expectations about the input enable the system to accurately interpret questions posed by the user in natural language and to retrieve context-relevant answers from a case base of answers, both video stories and textual information, to a wide range of common questions about planning a data analytics project. For example, the user might ask, “How can I determine the quality of data provided by a commercial data service?,” “What is the likely impact of poor data quality on my schedule?,” or “What is a reasonable expectation of the lag time between making a data request and receiving data from a market research firm?”

More important, perhaps, are situations in which the user does not recognize that a problem exists and, therefore does not think to ask a question, e.g., the question above about the likely lag in receiving data. In such situations, SPGU-T can use the same knowledge of task context and semantics of input information, coupled with heuristics for evaluating the completeness and reasonableness of information to proactively offer help and advice. SPGU-T can also carry information forward to a future task, for example, to offer proactive advice about the likely duration of the “data wrangling” step of an analytics project given previously entered information about the formats, quality, and lags in obtaining third-party datasets.

That being said, when SPGU-T is proactively offering help and advice, it is essential that it not be wrong if the user’s confidence in the value of such advice is to be maintained. In situations in which SPGU-T recognizes a likely problem with low certainty, it can do one of two things: It can offer a small set of potentially relevant pieces of advice from which the user can select, or it can ask the user a few questions to raise the certainty that specific advice is relevant to the user.

In either the case of answering a user’s question or proactively offering help and advice, SPGU-T can also answer follow-up questions, using not only the contextual information enumerated previously but also the user’s inferred intent in asking the follow-up question, thus making the retrieved answer all-the-more relevant.

There will, however, be cases in which SPGU-T cannot answer a question or cannot identify relevant help and advice with reasonable certainty even after interacting with the user to further understand his or her specific context. In such cases, SPGU-T will refer the question or situation to a human expert and promise the user that the expert will address the issue. SPGU-T can extend its case base as a result of capturing such interactions, thus enabling it to answer a wider range of questions and to provide better help and advice to future users.

We are building SPGU-T now. Watch this space.


Tuesday, February 14, 2017

Oh Accenture, you spent all that money and you learned nothing


Learning technology has its fads. One by one they are adopted by the big corporations who have one real goal: don’t spend a great deal of time on training people. 

In 1989, I was hired by Andersen Consulting (now Accenture) as a consultant to help improve their training which was mostly lecture based, at a training facility in St Charles, Illinois. Simultaneously, they gave Northwestern University a great deal of money enabling me to found the Institute for the Learning Sciences that tried to invent new ways of learning on the computer, and also took on ten Andersen people for two years master’s degree programs with the aim that they would bring back new ways of thinking about training to Andersen.

I happened to look at Accenture’s training site the other day:


I was curious what they were doing these days and wondering if I had had any effect on them. It was easy to draw two conclusions:

1- Accenture is now obsessed with the idea that all courses should last an hour and should be online. They are closing (or have already closed) the St Charles facility.

2. They learned exactly one thing from me. They learned that learning objectives for courses should be about doing rather than knowing. It doesn’t matter what people know (typically have memorized.) What matters is what people can do that they could not do before.

There seem to be hundreds of courses available. If you hit Risk Management, for example, about 100 course titles are listed. All of them seem to be one hour long (I didn’t look at every single one) and all of them have Learning Objectives that read like this one for Overcoming Challenges in Asset Management.

After completing this course, you should be able to:
Identify the top pressures and risks for asset management.
Recognize how top companies are addressing the growing talent gap.
Describe how to use remote asset connectivity for an effective asset management strategy.
Define how predictive analytics can strengthen your asset management strategy.



This uses the language of doing but objectives that start with “define how” are not really doing objectives.

And what does this one hour course involve the trainee in doing? It involves them listening to a one hour “webinar.” (The person giving this speech is working on a masters degree in statistics.)

Let’s look at another: Analyzing the Core Elements of the Strategic Plan. This one is one hour of online self study (which I suppose means one hour of reading). Its learning objectives are: 

After completing this course, you should be able to:
Determine how to create an effective mission statement at both the corporate and the divisional levels.
Recognize the role of objectives in the strategic planning process.
Identify the characteristics of an effective strategy.
Explain the function of tactics in a strategic plan.



I am impressed. I didn't know you could learn all that from an hour of reading. (The author of this course has an M.S. in Finance.)

So, clearly, I failed. I tried to change Accenture’s approach to training but failed. I already knew this because I hear from my former Andersen students from time to time and almost none of them are still at Accenture. And, ironically, my major clients are Accenture’s direct competitors. So, while I appreciate Andersen's help to me, so do their competitors. Accenture itself seems not to have learned much from me.

But, my real issue here, is trying to understand what you can learn and how you can learn, in an hour, since it this is now a fad that is driving demand for more and more one hour courses.

With this in mind, I asked myself what I might ever have learned in an hour (in a lifetime of trying and failing at many things.)

The first class (part of a graduate AI course) I ever taught (at Stanford in 1969) taught me a great deal in one hour. I learned how students at Stanford thought, what they paid attention to, and how Computer Science graduate students differed from me on what was important to think about. So I did learn a lot in hour. How?

I performed in front of people, tried to convey information by talking, and reflected on the kinds of responses I was getting back. Of course, I did this many times since I taught more than one hour of that course.

So, did I learn in an hour? Yes. Did I learn a lot? Yes. What motivated me to learn? I had to evaluate my own effectiveness.

In that same year, I learned something else in an hour. I taught one hour of a very different kind of course that was meant to encourage first year graduate students to sign up for a more intensive second semester course with the various faculty members who had that course. I was teamed with a guy named Ken Colby. He made people laugh when he talked. I didn’t. I had a lot to say in my hour. In the end our team signed up a large number of students. I was very proud of myself. I soon found out that they had all signed up because of him, not me. I asked him what I had done wrong. He said “you told them everything you know in one hour. If you can do that you don’t know much.” As you can see, I never forgot that. 

So, in effect I learned how to speak in an hour. One hour of failure and one comment from someone respected. I resolved to become a better speaker.  

My father once told me a story about how he had mistreated another lawyer when he was clerking after graduating from law school because that guy was dumb and he had graduated from a law school inferior to the one my father had attended. The punch line of his story was that that guy became the Chief Justice of the Supreme Court of New York. (My father hadn’t done much of note in his life.)

That was a lesson that was taught in ten minutes. But I never forgot it. In fact I learned two important things from that story. 

The first was not to be such a big shot because of what school you attended or how smart you are. I am sure that my father was unimpressed with how I had acted at some point which prompted the telling of the story.

The second thing I learned, after I reflected on this for some time, was the importance of just-in-time the story telling, which has served as the basis of my work in AI and in education.

The moral here is clear: stories are powerful and you can learn from them in a lot less than an hour, if, and only if, they are told by the right person at the right time.

So, I believe you can learn something in an hour. Here are some other things I learned in an hour. Each has a story associated with it, but in the interest of brevity I will omit the context of each:


1. Listen to what people tell you about themselves, they mean it.

2. In the academic world, be careful whom you attack.



3. Don’t assume you know the reason why things are the way they are. Dig into it and find out for yourself.


4. Politicians love to talk about education, but they really don’t give a damn.

  
5. Changing school is harder than simply making suggestions about what they should do.


6. You are not really encouraged to have your own point of view in college.


7. Life has its way of evening the score. 


8. Nobel Prizes aren’t awarded to revolutionaries. 

  
9. Genetics is powerful stuff. Most people don’t realize the extent to which their likes and dislikes and their ability to thrive under various conditions stems from thousands of years of evolution.


10. Smart is easy, but you are more likely to get a job by being smart and appearing to be cool. 

11. You know when you have won in sports. In real life, victory is never so clear cut.  

  
12. Education needs to be personalized and local at just the time when the country is trying to make it into one size fits all.

13. You can learn more by thinking about something and trying stuff out than you can by asking an authority for advice.

14. People rarely listen to the advice you give them


15. To produce great students help them to frame their questions and encourage hard answers. Asking a good question is much harder than answering one.  


16. Doctors seem to diagnose what they know, so find out what they know before you ask them whats wrong with you.   

  
17. Children are awful judges of their own childhood experiences, even as adults.


18. In the end, everyone just wants someone to pay attention to them. Good parenting is about paying attention while not overburdening the child with that attention.

19. Universities do not want to be “training school.” If you want to learn to do something practical, universities are probably not the place.  
  

20. You don’t really know what freedom is until you lose it.  

  
21. The goal of investors is to sell. The goal of inventors is to create. This always leads to conflicts.

All of these heave personal stories attached to them — experiences that taught me an important lesson in an hour.


So, can we build effective one hour courses? Yes. But they would have to not try simply to tell you something. They would need to put you in a situation you were in from which you could discover things about yourself and about the world.  Talking at people and telling them they will be learning to do things as a result of listening is simply wrong. We learn from actual experience and from reflection on that experience.


Here is a hint for Accenture. Instead of listing courses by their are and title, try cataloging the problems that you employees have on the job and create courses that help you resolve issues and problems that you have encountered. These shouldn't be listed alphabetically either. People think in terms of goals, and plans to achieve those goals, and problems they have encountered along the way.

Tuesday, January 31, 2017

My cousin Imre had to go to Canada when the U.S. kept him out. Worked out well for him. But, maybe that wasn't and isn't a great plan.

I don’t usually write political pieces, but today’s news reminds me of my cousin Imre. He grew up in what is now Slovakia, escaped to London as part of the government during the war and returned home to help after the war. He was forced to leave Czechoslovakia after the Russians arrived. He tried to emigrate to the U.S. He was denied entry. His uncle, my grandfather, had lived in the U.S. for over 40 years, but there was nothing my grandfather could do to get him. He visited him in New York in 1946 when he was concerned about staying in Europe  but was sent back. Canada let him in. He was always “Imre from Canada” to me. (There was also an “Imre from Vienna” in our family.)
As someone who now lives in Canada in the summer, I realize that nearly every Jew that I meet there has the same story. Their family was denied entrance to the U.S. in the 30’s and 40’s, so they went to Canada.
We have had presidents who stopped immigration for reasons best left to their own reasoning. I am no fan of Franklin Roosevelt, but I note that he never gets vilified for this. Today, we are very worried about Mr. Trump’s actions. I can say in his defense that we ought to be used to Presidential decisions that affect people badly. (Would we have ISIS today if Woodrow Wilson had kept us out WW1?) Maybe Mr. Trump is right. Who knows? Mr Roosevelt wasn't right. Many members of my family died because of his decisions. Keeping my cousin Imre out of the U.S. was Canada’s gain. 

Here is a link to a site about him:

Here is an excerpt about his life taken from this: 
pastedGraphic.png 





lmrich Yitzhak Rosenberg. Bom in NoveMesto,Slovaki& May22,1913. Active in national Jewish youth movement. Doctorate in government and law, Bratislava University, 1939. After two interruptions to work underground against Hitler. While attending the Academy for International Law in The Hague, he helped organize the escapes of Jews from Czechoslovakia and Berlin in 1939. "I landed in England the day before the war broke out, to buy a boat to move refugees," he recalls.  "Every decent person who was safe in London wanted to join a unit fighting Hitler." He joined the Czech army and helped build the London-based resistance movement, led by Edvard Benes, which was eventually recognized as the legitimate democratic government- in-exile in Czechoslovakia. He headed for Czechoslovakia as the war ended, travelling with the Soviet Army as it liberated concentration camps in Poland, Germany, Austria and Czechoslovakia. "The world that had been closed in by the Nazis was being opened up. I was one of the first people to visit Theresienstadt camp, north of Prague, and I selected 301 orphaned children for transfer to England. The British Home Office sent 16 planes to pick them up. I also chose adults, most of them already with relatives in England, to accompany the children - about one for every ten children." There was also a steady stream of Jews arriving in Czechoslovakia from camps in Germany's Ruhr valley. "I don't think there is another living person who has seen as much as I in terms of broken people who survived the camps." He says now in a tone of wonder, "I was a young man when I was doing these things -today I think it was impossible. You're dealing with 180,000 people moving across the border. We gave them medical help, money, tickets. I heard about (Foreign Minister Jan) Masaryk's death from a street cleaner at seven in the morning, though the government didn't announce it until the afternoon. He was pushed from a window; there is proof. I slipped out of the country but my first wife made a mistake and she was caught. She was in jail, terrible jail, for twelve years." Having learned he'd been sentenced in absentia to life imprisonment, Rosenberg spent a year in England waiting for a Canadian visa.   When he arrived in Ottawa as a landed immigrant he was turned down by the Civil Service Commission and instead worked as a laborer, carrying vegetables in Byward Market. Eventually he started lecturing at Ottawa University and selling houses, becoming a partner in a successful real estate company. Along the way he donated $12,000 to establish a home for international students and public servants ("so others would not be left out in the cold in Ottawa, as I was”.)  


I believe in vetting potential immigrants. But there are many potential Imre Rosenberg’s out there. Let’s give them the chance that FDR didn’t give my cousin Imre.

Monday, January 23, 2017

To fight a Cyber War we need to train more people



I am of the age where the major preoccupation in my youth was avoiding having to go fight in the Viet Nam War. Ironically, during those years I was supported in my work by the U.S. Defense Department (DOD). I justified doing this because I wasn’t helping anyone kill anyone. If my work was to be useful to DOD at all, it would be helping us defend ourselves.

But now I am working with DOD on offense. What happened?

In the last year I have become more involved with cyber security. Why? I, and the people who work for me, primarily build online learn by doing courses (using live mentors to help when students are confused and to provide feedback on their work. When the DOD began talking to me about building a cyber operations course for them, I was interested. Developing new ways to learn is my business after all. So, I listened. I interviewed hackers employed by DOD and other federal agencies,  attended DefCon (a hacker convention held in Las Vegas) and over time I recognized what plenty of other people already knew. 

Here are some recent news stories I found about cyber attacks:

Today: Lloyds cyber-attack details emerge


Today: As attacks grow, EU mulls banking stress tests for cyber risks


Two weeks ago: Ukraine power cut 'was cyber-attack’


Two weeks ago: London NHS hospital trust hit by cyber-attack


Two weeks ago: Indian banks are waking up to a new kind of cyber attack 

 http://economictimes.indiatimes.com/industry/banking/finance/banking/indian-banks-are-waking-up-to-a-new-kind-of-cyber-attack/articleshow/56575808.cms



Three Weeks ago: U.S. Grid in ‘Imminent Danger’ From Cyber-Attack, Study Says


This is a serious issue and I want to help. We are, right now, building a course in cyber security. The Pentagon has a serious problem. Here is what Frank DiGiovanni, the Director of Force Training in the office of the Assistant Secretary of Defense for Readiness has to say: "The security of our nation is at stake. I think it’s imperative for DoD to embrace the hacker community because of the unique skills they bring to the table. They want to serve and contribute, and the nation needs them.”

This is from POMNEWS.net:

DiGiovanni built an instructor led course, but there are limits to how many people can be taught face to face. Lecturing is not a really effective method for learning how to do something. We learn how to do things with one on one mentoring and we learn from trying and failing. 

DiGiovanni knows this:
“We infused the course with sociology, ethnography and anthropology.… You don’t conduct an assault on the enemy if you don’t know the terrain they’re in, what surrounds them.”

The social science disciplines help students better understand who they’re up against and why. Those facts can then be aligned with what we know of adversary’s signature techniques, tactics, and procedures.
“Techniques give clues about who they are and could also tip off what you’re after,” DiGiovanni says. This includes the way adversaries might seek to cover their tracks. For example, Russia adapted the concept of maskirovka – literally, masking –from conventional battlefield usage and applied it to the cyber arena. Students learn to identify the tactics of different adversaries, as well as the techniques that can be employed to cover one’s tracks. They have to become adept at identifying what the adversary is doing as well as executing their own cyber missions without leaving digital fingerprints in their wake.

“The biggest complaint about journeyman-apprentice is: It doesn’t scale,” DiGiovanni says. That makes it more costly and slower, compared to traditional teaching methods. Journeyman-apprentice is another core concept built into this course.

DiGiovanni doesn’t want to ditch the approach, just find a way to make it more efficient.

So, DOD contracted with my company to build a course to train tens of thousands.

We will help the military fill its large need for hackers by creating hackers. DOD wants to teach offense and defense. We can’t just simply sit back and defend, we have to frighten the enemy to stop as well as get into their systems. This is a lot like building missiles to defend against missiles.  

It crossed my mind that I could re-employ the defense part of the course and use it to train people who work for companies that may be subject to attack. I discussed this with one of the hackers whom we rely on as an subject matter experts in our course. I was told that I had it wrong. In fact, I had a lot wrong (after all why would I know?) 

Some stuff I (and most people) had wrong.

  1. Students would need to be people who can program (not true)
  2. Companies can hire the people they need. (They can’t be found)
  3. There must be some existing courses to train more (There are but they are short, or lecture based, or generally like most courses that try to teach complex skills quickly without using learning by doing with lots of practice and help.)  
  4. Businesses need defenders not attackers (This is completely wrong because some of the best cyber people are penetration testers who break into their own company’s systems to find out where they are vulnerable.)



We have developed just enough right now to be able to try it out on people who want to help. We are finding the oddest of people who want to do this (a massage therapist, an acupuncturist (OK, she was a computer scientist before she retired), a recent H.S graduate taking a gap year, and the former head of research at a big consulting firm. They are getting good at this and love it. (You need to be someone who gets into complex puzzles and generally thinks breaking into things is fun.)

We have a public website (which is changing very day) if you want to see more.




Below is a something from the first page of the course which lists what students will learn to do:







I am excited about this because I think it matters. Personally, I like having electricity and knowing that my money is secure when I use a bank.