COMMENTARY
This series of posts from Vinod Khosla are both inspiring and scaring. A computer working with data can get a mathematically correct answer based on the data that the computer is able to access ... and the methods for doing the mathematics are now highly evolved from the perspective of a mathematician.

But I have a very simple problem with the data and the architecture of the problem, and therefore I have a problem with the solution. Maybe the computer can compensate, but I am not so sure.

All the data are based on rather conventional ideas about the data that should be available. In the narrow area of people. medical health and the 'sickness' sector, the issues may be quite simple, but when the data are related to the broader issue of health of society and economic health, then the data available may be not at all helpful. The data that dominates this sector are about profit and GDP growth and the related statistics, with rather little about quality of life and what goes into improving quality of life. There has been more than 50 years of teaching students how to improve corporate business performance, and esentially no work on teaching how to improve quality of life. Yes ... it is good that Bhutan has initiated a National Happiness Index, but where does Bhutan rank in the global economic power structure ... and the answer is a long way below the United States and Europe and the BRICS and most every other nation on the planet. I admire Bhutan, but they do not have the same leverage as the US and the other OECD countries!

If we use computers to optimize what the Business Schools have taught people over the past fifty years, the answers are going to be dangerous for society, the economy and the planet ... but if we can use computers to optimize what Bhutan has introduced, then the planet has hope.
Peter Burgess

The Surprising Path Of Artificial Intelligence
VINOD KHOSLA ... Monday, January 9th, 2012
http://techcrunch.com/2012/01/09/khosla-artificial-intelligence/

Editor’s note: This is Part I of a three-part guest post written by legendary Silicon Valley investor Vinod Khosla, the founder of Khosla Ventures. In Part II, he will describe how software and mobile technologies can augment and even replace doctors. In Part III, he will talk about how technology will sweep through education.

Forty years ago this December, President Nixon declared a war on cancer, pledging a “total national commitment” to conquering the disease. Fifty years ago this spring, President Kennedy declared a space race, promising to land a man safely on the moon before the end of the decade. And 54 years ago, Artificial Intelligence pioneer Herbert Simon declared “there are now in the world machines that think” and predicted that a computer would be world chess champion within 10 years.

1. i) a post on bionic software defined as one that combines the biological and mechanical systems to create an enhanced system that is more powerful than either alone. “Bionic software,” in this sense, is the interplay of humans and computers augmenting each other’s actions and amplifying one another’s understanding (for a while at least, they can help and complement each other);
2. ii) a recent Forrester assessment that the iPad 2 in 1993 would be considered one of the top 30 most powerful computers on the planet and similar to a Cray supercomputer from 1986.
3. iii) An Nvidia graphics chip designed primarily for video games today can be assembled into a five teraflop machine for less than $25,000 and can happily run powerful new programs (among many other types) called “self organizing machines” that determine the how (algorithms for geeks) if told the “what” (examples of right/wrong medical diagnosis or fraudulent transactions or unusual patterns of behavior or symptoms, for example).
4. iv) finally, the fashion in big data in the venture capital business has injected a lot of new energy and exploration for using experts and expert systems, probability and statistics, machine learning, self organizing machines and many less-discussed and some yet-undisclosed systems. (Those stock traders seldom talk about what their computerized trading systems do, nor do the spooks or Google in its spam filtering algorithms). Data, and big data especially hold “truths & likely correlations” well beyond the biases of your average doctor, and unaffected by the distortions of pharmaceutical marketing and selective medical study reporting, or a desire to make more money, or other intentional and unintentional human failings.

Do We Need Doctors Or Algorithms?
VINOD KHOSLA ... Tuesday, January 10th, 2012
http://techcrunch.com/2012/01/10/doctors-or-algorithms/

Editor’s note: This is Part II of a guest series written by legendary Silicon Valley investor Vinod Khosla, the founder of Khosla Ventures. In Part I, he laid the groundwork by describing how artificial intelligence is a combination of human and computer capabilities. In Part III, he will talk about how technology will sweep through education.

Later, I got to cogitating about what I had said and why, and how embarrassingly wrong that might be. But the more I think about it the more I feel my gut reaction was probably right. The beginnings of “Doctor Algorithm” or Dr. A for short, most likely (and that does not mean “certainly” or “maybe”) will be much criticized. We’ll see all sorts of press wisdom decrying “they don’t work” or “look at all the silly things they come up with.” But Dr A. will get better and better and will go from providing “bionic assistance” to second opinions to assisting doctors to providing first opinions and as referral computers (with complete and accurate synopses and all possible hypotheses of the hardest cases) to the best 20% of the human breed doctors. And who knows what will happen beyond that?

Assessing Current Healthcare

Let’s start with healthcare (or sickcare, as many knowledgeable people call it). Think about what happens when you visit a doctor. You have to physically go to the hospital or some office, where you wait (with no real predictability for how long), and then the nurse probably takes you in and checks your vitals. Only after all this does the doctor show up and, after some friendly banter, asks you to describe your own symptoms. The doctor assesses them and hunts around (probably in your throat or lungs) for clues as to their source, provides the diagnosis, writes a prescription, and sends you off.

The entire encounter should take no more than 15 minutes and usually takes probably less than that. Sometimes a test or two may be ordered, if you can afford it. And, as we all know, most of the time, it turns out to be some routine diagnosis with a standard treatment . . . something a computer algorithm could do if the treatment involved no harm, or at least do as well as the median doctor (I am not talking about the top 20% of doctors here—80% of doctors are below the “top 20%” but that is hard for people to intuit!).

So what’s wrong with this situation? This is by no means an exhaustive list, but it sets up a nice springboard:

• Physically having to go to your doctor’s office makes sense for the most part, except that a lot of the basic tests are either visual (tongue and throat check) or auditory (listening to the breath and vibrations in the abdomen). Time plus cost will often discourage people from taking that first step to visit a doctor. Most of the time a Dr. A could at least advise you when it is worth visiting based on your normal body functions, your current indications, and your locality’s current infections and other symptom trends.
• A lot of the vitals being tested for (e.g. blood pressure, pulse) can now be routinely done at home or even with the help of an iPhone and an explosion of additional possibilities will emerge in the next decade.
• You are the one telling the doctor your symptoms.
• The doctor has to inquire (probably every time) into any possible history of each symptom, test results, and illnesses, except when he does not have time for you in that village in India.
• The prescriptions are still done on paper, requiring you to, again, physically go to a pharmacy and pick up what you need there. So compliance is an issue.
• Looking at this, I cannot help but think that this is a completely antiquated system (regardless of whether it is healthcare or not)!

The history of symptoms, illnesses, and test results could be accessed, processed, and assessed by a computer to see any correlation or trends with the patient’s past. You are the one providing the doctor with the symptoms anyway after all!

Any follow-up hunts for clues could again be done with mobile devices. The prescriptions—along with the medical records—could relocate to electronic and digital methods, saving paper, reducing bureaucracy, and easing the healing process. If 90% of the time the doctor knows exactly the right kind of diagnosis from these very few and superficial inputs (we haven’t even considered genetics yet!), does it really require 10+ years of intense education for every diagnostician?

The fault is not entirely with the doctors, though. Most of us don’t know what set of symptoms warrant the full-scale attention of medical personnel, so we either go all the time or we do not go at all (save for emergencies). We also cannot realistically expect any (even our family) doctor to remember every single symptom and test result over the years, definitely not in a government hospital in China. Similarly, we cannot expect our doctor to be able to remember everything from medical school twenty years ago or memorize the whole Physicians Desk Reference (PDR) and to know everything from the latest research, and so on and so forth. This is why, every time I visit the doctor, I like to get a second opinion. I do my Internet research and feel much better.

Identifying Emerging Trends In Healthcare

But I always wonder why I cannot input my specific test numbers and have a system offer me a “second opinion” on the diagnosis since it has all the data that the doctor has and can use all my current and historical data effectively. In fact, it is not hard to imagine it having more data than the doctor has since my full patient record would be at the tip of its digital brain, unlike the average doctor who probably doesn’t remember my blood glucose levels or my ferritin from two years ago. He does not remember all the complex correlations from med school in which ferritin matters—there are three thousand or more metabolic pathways, I was once told, in the human body and they impact each other in very complex ways. These tasks are perfect for a computer to model as “systems biology” researchers are trying to do.

Add to it my baseline numbers from when I was not sick, which most doctors don’t have and if they did 80% of physicians would be too lazy to use or not know how to use. Applied Proteomics can extract tens of gigabytes of proteomics—what my genes are actually doing instead of what they can do—baseline data from one drop of blood. Oh, by the way I have my 23andMe data to add my genetic propensities (howsoever imprecise today, but improving rapidly with time and more data). The doctor uses a lot of imprecise judgments too as most good doctors will readily admit. My very good doctor did not check that I have relative insensitivity, genetically, to Metformin, a diabetes drug. It is easy to input the PDR (the Physicians Desk Reference), the massively thick, small-font book that all physicians are supposed to know backwards and forwards. They often don’t remember everything they read, in med school but it is a piece of cake for computers. The book on your typical doctor’s desk is probably not current on the leading-edge science either. Confirmed science and emerging science are different things and each has a role. Doctors mostly use confirmed science, the average doctor not understanding and pros and cons of each or the expected value of a treatment (benefit and harm). And our 18th century tradition of “first do no harm” dictates that if a treatment hurts ten patients a year but saves a thousand lives we reject it.

With enough examples, today’s techniques for language translation (or newer techniques) can translate from human lingo for symptoms (“I feel itchy” or “buzzy” or “reddish bubbly rash with pimples” or “less energy in the morning” or “sort of a stretch in my tendon” and the myriad of imprecise ways symptoms are described and results interpreted — these are highly amenable to big data analysis) into medical lingo matching the PDR. With easy input of real medical results into a computer and long-standing historical data per patient and per population, which a human cannot possibly handle, and patient and population genetics, I suspect getting a second opinion of my diagnosis from Dr. A is a reasonable expectation, and it should certainly be better than a middling physician’s (especially in less developed countries like India, where there is a dire shortage of trained physicians).

I may still need a surgeon (though robotic surgeons like those from Intuitive Surgical are on the way too) or other specialists for some tasks for a little while and the software may move from “second opinion” (in three years? Or seven?) to “bionic software” for the physicians (in five or ten years, with enough patient data?). Bionic software, again, defined here as software which augments and amplifies human understanding.

But I doubt very much if within 10-15 years (given continued investment and innovation and keeping the AMA from quashing such efforts politically) I won’t be able to ask Siri’s great great grandchild (Version 9.0?) for an opinion far more accurate than the one I get today from the average physician. Instead of asking Siri 9.0, “I feel like sushi” or “where can I dispose a body” (try it…it’s fairly accurate!) and with your iPhone X or Android Y with all the power of IBM’s current Watson computer in the mobile phone and an even more powerful “Nvidia times 10-100” server which will cost far less than med school with terabytes or petabytes of data on hundreds of millions (billions?) of patients, including their complete genomics and proteomics (each sample costing about the same as a typical blood test).

IBM’s Watson computer, I understand, is now being applied to medical diagnosis after handling imprecise and vague tasks like winning at Jeopardy, which experts a few years ago would have said could not be done. “Computers cannot match the judgment of humans on these kinds of tasks!” And with enough data, medical diagnosis or 90% of it is an easier task than Jeopardy.

Already Kaiser Permanent already has 10 million real-time medical records with details of 30,000,000 e-visits last year with caregivers and computer modeling of key diseases per individual that data scientists would love to get their hand on. Already, according to IDC 14% of the US population is using their phones for medical help and 200 million health and fitness related mobile applications have been downloaded according to pyramid research. Fun stuff, though early. They are probably two generations away from systems that are actually useful.

A more elaborate vision, one that is not very useful today because of lack of enough data and enough science, is defined in Experimental Man and websites like Quantified Self. Though they feel like toys today, they are much further along than the mobile phone was pre-iPhone in January of 2007. And data, the key ingredient to useful analysis, and diagnosis, is starting to explode exponentially—be it genetic data, proteomic data or physical data about my steps, my exercises, my stress levels or my normal heart and respiration rates.

My UP wristband or something like it (disclosure: I am an investor in Jawbone)) will know all my sleep patterns when I am healthy and how many steps I take each day and may have more data on my mobility if I ever get depressed than any psychiatrist ever will know what to do with. Within a few years, my band will know my heart rate at all times, my respiration rate, my galvanic skin resistance (one parameter among multiple ones used to measure my stress level), my metabolic rate (should cost about $10 to add to the band by measuring my CO2 in my breath and may detect changes in my body chemistry too like when I get a certain type of cancer and traces of it show up in my breath).

All my “health data” as well as my “sick data” and my “activity data” will be accessible to Dr. A (and location when I was stressed or breathing hard or getting the allergic reaction and what chemicals were nearby or in the air—did toluene exposure cause me to break out in a rash from that new carpet or trigger a systemic reaction from my body?). I doubt I will be prescribed an arthritis medicine without Dr. A knowing my genetics and the genetics of my autoimmune disease. Or a cancer medicine without the genetics of my cancer when the genetic sequence (once per life) costs far less than a single dose of medicine. In fact all my infectious disease treatments may be based on analysis of my full genome and my history of exposure to viruses, bacteria and toxic chemicals.

Constant everyday health data from non-medical devices will swamp the “sickness tests” used in most medical diagnosis and be supplemented by detailed genetic, proteomic and sick data with bionic software and machine learning systems. Siri might even remind me one day that my heart rate while sleeping has gone up abnormally over the last year, so I should go run some heart sickness cardiograms or imaging tests. Obviously, Siri’s children and its server friends will be able to keep up with the latest research and decide on optimal strategies based on patient preference (“I prefer to live longer even if it means all the fancy treatments” or “I want to live a normal life and die. I prefer to spend more of my time with my children than at the hospital” or “I like taking risky treatments”). They will take into account known research, early pioneering approaches, very complex interrelationships and much more.

My best guess is that today a physician’s bias makes all these personal decisions for patients in a majority of the cases without the patient (or sometimes even the physician) realizing what “preferences “ are being incorporated into their recommendations. The situation gets worse the less educated or economically less well-off the patient is, such as in developing countries, in my estimation.

Envisioning Future Healthcare

Eventually, we won’t need the average doctor and will have much better and cheaper care for 90-99% of our medical needs. We will still need to leverage the top 10 or 20% of doctors (at least for the next two decades) to help that bionic software get better at diagnosis. So a world mostly without doctors (at least average ones) is not only not reasonable, but also more likely than not. There will be exceptions, and plenty of stories around these exceptions, but what I am talking about will most likely be the rule and doctors may be the exception rather than the other way around.

However fictionalized, we will be aiming to produce doctors like Gregory House who solve biomedical puzzles beyond our best input ability. And India, China and other countries may not have to worry about the investment in massive healthcare or massive inequalities in the type of physicians they might have access to. And hopefully our bionic software (or independent software someday) will be free of the influence of heavily marketed but only minimally effective drugs or treatment regimes or branding campaigns against generics or lower-cost and equally effective, more affordable drugs and treatments. Dr. A will be able to do a cost optimization too both at the patient level and at the policy level (but we may choose, at least for a decade or two, to reject its recommendations—we will still be free to be stupid or political).

What is important to realize is how medical education and the medical profession will change toward the better as a result of these trends. The vision I am proposing here, though, is one in which those decades of learning and experience are used where they actually matter. We consider doctors some of the most learned people in our society. We should aim to use their time and knowledge in the most efficient manner possible. And everybody should have access to the skills of the very best ones instead of only having access to the average doctor. And the not so “Dr. House’ doctors will help us with better patient skills, bedside manners, empathy, advice and caring, and they will have more time for that too. If computers can drive cars and deal with all the knowledge in jeopardy, surely their next to next to next…generation can do diagnosis, treatment and teaching in these far less uncertain domains and with a lot more data. Further the equalizing impact of both electronic doctors and teaching environments has hugely positive social implications. Besides, who wants to be treated by an “average” doctor? And who does not want to be an empowered patient?

The best way to predict this future is not to extrapolate the past and what has or has not worked, but to invent the future we want, the one we believe possible!

Editor’s note: This is Part III of a guest post written by legendary Silicon Valley investor Vinod Khosla, the founder of Khosla Ventures. In Part I, he laid the groundwork by describing how artificial intelligence is a combination of human and computer capabilities In Part II, he discussed how software and mobile technologies can augment and even replace doctors. Now, in Part III, he talks about how technology will sweep through education.

In my last post, I argued that software will take over many of the tasks doctors do today. And what of education? We find a very similar story of what the popular – and incredibly funny! – TED speaker Sir Ken Robinson calls “a crisis of human resources” (Click here for the RSA talk from the same speaker which has been animated in a highly educational fashion). At the TED 2010 conference, he stated that “we make poor use of our talents.” Indeed, in the same way that we misuse the talents and training of doctors, I believe we misuse the talents and training of teachers.

I want to comment on what I consider a far greater misuse of talent and training: that of our children/students, mostly here talking about high school education. We have focused so much of our education system on children attending primary school, then middle school, then high school, all with the objective of attending university. This is a progression that still remains unchanged and largely unchallenged. Yet, this system is completely linear and, most tragically, unwaveringly standardized not only through instruction methods, but also through testing. Worse, it is mostly what I call “fixed time, variable learning” (the four-year high school) instead of “fixed learning, variable time” to account for individual students’ capabilities and status.

Identifying Emerging Trends In Education

There are new key trends that I see emerging in education enabled by advancing technology: namely decentralization and gamification. By understanding these trends, it is much easier to imagine why we won’t need teachers or why we can free up today’s teachers to be mentors and coaches. Software can free teachers to have more human relationships by giving them the time to be guidance counselors and friends to young kids instead of being lecturers who talk at them. This last possibility is very important—in addition to learning, schools enable critical social development for children through teacher student relationships and interacting with other children—classrooms of peers and teachers provide much more than math lessons. And by freeing up teachers’ time, technology can lead to increased social development rather than less as many assume.

Still, nearly all the attempts at technology in education have mostly failed so far, but I doubt they will continue to fail. I believe the failures have been failures of tactics rather than failures of strategy. In other words, just because some social networking sites like Friendster and Myspace failed does not mean that all social networking sites (like Facebook) will fail!

Let’s start with decentralization, which involves not only making content available online but also producing content that is interactive and mobile. It’s encouraging to me that we are starting to seriously experiment with content that is different than linear translations of books to online. With the new platforms, we have the ability to rapidly run experiments with new styles, techniques and resources (like social learning) which will lead to a new understanding of education.

At a very simple level, organizations like Khan Academy are making up for students who have bad teachers by starting with good lectures on every topic. And it seems to be working; hundreds of thousands of students are already accessing these videos, making up for what is lacking (likely from their “average teacher” – on the other hand good teachers, the top 20%, like great doctors, will always be in demand, and though each of us can tell stories about an awesome teacher, anecdotal counterexamples to my assertions are not “statistical proof” of the general quality of teachers). Meanwhile organizations like my wife’s CK12.org are making the basic content for high school education free and continuously improving (because they are open source).

This decentralization does not have to benefit only the students, though. Coming next from CK12, early in 2012, for example, will be lesson plans for teachers to access, “bionic software” to help them assess their students (Khan is also adding this to their system). The CK12 system in 2012 will also allow for students to teach themselves from a concept map of all 5000 or so concepts that each student needs to know to qualify to get into MIT or Stanford (the number of concepts depends on granularity and is mostly semantics). CK12 will allow teachers to guide individuals or for students to guide themselves while being aligned with state (or country) curriculum requirements using any of several different learning modalities (text, video explanations, experimentation, labs, playing with Flexmath, simulations, Q&A bank for students or teachers to self-test, social Facebook-like help for students, and teachers and much more). Systems such as these will enable near universal learning (again with some exceptions). There are numerous other examples.

The important thing is not the specific first instantiations of these systems but that they offer a customizable playground for low-cost experimentation. Today, to try a new experiment in education in the US means starting a new school, training new teachers and taking years and tens of millions of dollars. My hope is that environments like that of CK12.org (and I see many more bubbling up, too numerous to mention here) will decrease the cost of experimentation and hence dramatically increase the amount of experimentation. This will result in accelerated innovation in education well beyond anything that has happened in the past.

The universal availability of inexpensive web access devices like tablets and smartphones and new trends like gamification and social software will surely add to the acceleration. Meanwhile the ability to collect data online, at a scale not possible in traditional systems will help us better understand student behavior. When every click and every hesitation at every stage of every reading, assignment or problem is available to analyze with big data techniques, we will finally understand at an exponentially faster rate what works and what does not.

The other trend I am excited about is gamification. When parents think of games, they usually consider them a waste of time. More importantly, they consider them a waste of talent. The debate about the place of games in learning rages on, but one aspect of gaming is unequivocally clear: it’s sticking around. Therefore, I firmly believe that we should embrace it and harness its best parts to drive the education of our children who grow up with online and mobile games. And I really mean, grow up with it! In a NYT article also from last year, according to research by the Joan Ganz Cooney Center, 60 percent of the top-selling iPhone apps on the education store are made for toddlers and preschoolers. Do we expect these children to relinquish and forget their app- and game-centered development after they get to first grade? This is completely unreasonable! And for me it is easy to envision how we can make education more engaging with these approaches, hence enhancing learning at all levels be it kindergarten or medical school. There is sufficient early evidence to suggest these assertions will be proven correct. I am a fan of views like those of Jane McGonigal whose #1 goal in life (quoting her website) is to see a game designer nominated for a Nobel Peace Prize because of the impact game design can have on humanity.

I am particularly excited about the possibilities when high school education moves from teachers talking uniformly to bored A students and clueless D students, fifty to a class, to individual, gamified, and adaptive-difficulty systems, that leverage our social inclinations as demonstrated by Facebook. Imagine friends helping us understanding subjects while they also understand our context. Both the students helping and the ones being helped are likely to understand the subject matter better in my view. And with points and stars and badges and the like both are likely to want to spend more time participating, and will be more motivated when they do participate compared to today’s average classroom. Add reputation systems to that and one has the beginnings of a revolution. The content to train the trainers will be produced by some of the top 20% of teachers, and over time technology will multiply the impact and reach of these top teachers, motivating the rest of the best to participate as well. Other motivated teachers can feel free to jump in while the rest can go enjoy their favorite TV show.

Envisioning Future Education

Can you imagine an educational platform like CK12 version 9.0 in ten years, for example, with the excitement-generating, attention-grabbing, and skill-building potential of a Zynga game times ten? Can you imagine an environment based around a game? The awkward early prototype example of this (that will get much better in its multi-generational evolution) is Quest to Learn. Rather than pushing education on its students, the teachers pull the students into education through a game-like progression exploring 21st-century skills such as code-based problem solving, social media generation and integration, and design through games. The beginnings of these future trends of educational institutions and platforms are, therefore, already in place.

One other critical piece in my vision of the future is (re)-discovering the potential of each student as just that – a student. Pioneering social experiments such as Hole in the Wall have shown us once more in explicit terms the incredible ability of children to learn if self-motivated. Children who have never seen – much less operated! – a computer, were able to learn how to browse the web, play games, learn the basics of a foreign language, and read manuals to the software in the computer. All of this within the timeframe of less than three months. Most of these tasks, they accomplished within hours of playing around with the machine!

More importantly, they were then able to teach themselves and others in their community. Children have the natural ability to learn and teach. With socialization, big data analytics and gamification as helpful tools, the future of education lies in providing children with an environment in which they can learn in their own way, at their own pace, and their preferred style/methodology/modality. I suspect they will still be able to meet any state or university curriculum standards. I could even imagine each university defining its own standards, providing the ultimate customization that no typical school today could. We may not need as many doctors as we have today but I suspect there is still a major role for the 80% of teachers who are not in the top 20%. They can provide the “human touch” and be mentors and coaches. Maybe teaching will become interesting enough to attract more teachers!

So is it possible to imagine solving the healthcare and education problems without doctors and teachers in their traditional roles within a decade or two or three? (Timing is always far off and the technologists always over-estimate the near term while underestimating the long-term because of the exponential nature of progress that builds on each previous step). As I’ve mentioned before, if computers can drive cars and master all the knowledge required to win Jeopardy, then surely it won’t be long before they will be able to diagnose disease and teach high school. With more and more data, these teaching and healing algorithms will keep improving and will free up human teachers and doctors to do what they do best.

Technology can allow us to make better use of our natural human resources, be they related to our health or to our education. Empowering patients to understand themselves better through continuous and comprehensive data and enabling students to develop themselves through accessible and attractive environments…this is the future I see. And if I can see it from these emerging trends, the key takeaway, then is, this: if we can see it, we can most certainly grasp it. All we need to do to reach this future is to invent it.

Illustration by koya979