Keynote talk of Anita Schjoll Brede @twitnitnit @oebconference #AI #machineLearning #oeb18

(liveblog starts after a general paragraph on the two keynotes that preceded her talk, and really her talk was really GREAT! And with fresh, relevant structure).

First of a talk on the skill sets of future workers (the new skills needed, referring to critical thinking, but not mentioning what is understood with critical thinking) and the collective intelligence (but clearly linking it to big data not small data, as well described in an article by Stella Lee).

Self-worth idea for the philosophy session, refer tot he Google map approach where small companies who offer one particular aspect of what it took to build google maps were bought by Google, and as such producing something that was bigger than the sum of its parts). But this of course means that the identity and the self-versus-the-other becomes under pressure, as people that really make a difference at some point, do not have the satisfying moment to think they are on top of the world (you can no longer show off your quality easily… for there are so many others just like you… as you can see when you read the news, follow people online…). While feeling important was easier, or possible in a ‘smaller’ world, where the local tech person was revered for her or his knowledge. So, in some way we are loosing the feeling of being special based on what we do. Additionally, if AI enters more of the working world, how do we ensure that work will be there for everyone, as work is also a way to ‘feel’ self-worth. I think keeping self-worth will be an increasing challenge in the connected, and AI supported world. As a self-test, simply think of yourself, and wanting to be invited to be on a stage… it is a simple yet possibly mentally alarming aspect. Our society is promoting ‘being the best’ at something, or having the most ‘likes’, what can we do to install or keep self-worth?

Than a speaker on the promise of online education, referring to MOOCs versus formal education, the increase of young people going to college… which strangely contradicts what the most profiles of future jobs seems to be like (professions that are rather labour intensive). The speaker Kaplan managed to knock down people who get into good jobs based on non-traditional schooling (obviously, my eye-brows went up, and I am sure there are more of us in the audience pondering which conservative thinking label can be put on that type of scolding stereotype speech, protecting the norm, he is clearly not even a non-conformist).

Keynote by Anita Schjoll Brede 

Here a person in the line of my interest takes the stage: Anita  Schjoll Brede. Anita founded an AI company Iris.ai , and tries to simplify the AI, machine learning and data science for easier implementation. So… of interest.

Learning how to learn sets us human beings apart. We are in the era where machines will learn, based on how we learn… inevitably changing what we need to learn.

She gives what AI is seen by most people, and where that model is not really correct.

Machine learning is based on the workings of a human brain. Over time the machine will adapt based on the data, and it will learn new skills. It is a great model to see the difference. One caveat, we still not sure how the human mind really works.

If we think of AI, we think of software, hardware, data … but our brains are slightly different and our human brains are also flawed. We want to build machines that are complementary to the human brain.

Iris.ai started with the idea that there are papers and new research published every day, humans can no longer read all. Iris.ai goes through the science and the literature process is relatively automated. The process is currently possible with a time decrease of 80%. Next step is hypothesis extraction, than build a truth tree of the document based on scientific arguments. Once you have the truth trees are done, link that to a lab or specific topic, … with an option of the machine learning results leading to different types of research. Human beings will still do the deeper understanding.

Another example is one tutor per child. Imagine that there is one tutor for that child, which grows with that child, helps with lifelong learning. The system will know you so well, that it will know how to motivate you, or get you forward. It might also have filters to identify discriminatory feelings or actions (remark of myself: but I do wonder, if this is the case, then isn’t this limiting the freedom of saying what you want and being the person you want to be… it might risk becoming extreme in either way of the doctrine system).

Refers to the Watson Lawyer AI, which makes that the junior lawyers will no longer do all the groundwork. So the new employees will have to learn other stuff, and be integrated differently. But this relates to critical ideas of course, as you must choose for employing people (but make yourself less competitive) or you only higher senior lawyers (remark of myself: but than you loose diversity and workforce).

Refers to doctors built by machine learning, used in sub-Saharan settings, to analyse human blood for malaria. Which saves time for the doctors, health care workers… but evidently, this has an impact on the health care worker jobs.

Cognitive bias codex (brain picture with lots of links). Lady in the red dress experiment.

Her take on what we need to learn:

Critical thinking,  refers to source criticism she learned during her schooling.

Who builds the AI, lets say Google will transgress the first general AI… their business model will still get us to buy more soap.

Complex problem solving: we need to hold this uncertainty and have that understanding. To understand why machines were lead to specific choices.

Creativity: machines can be creative, we can learn this. Rehashing what is done, and making it to something of your own is something that is (refers to lawyer commercial that was built by AI based on hours of legal commercials).

Empathy: is at the core of human capabilities like this. Machines are currently doing things, but not yet empathic. But empathy is also important to build machines that can result in positive evolutions for humans. If we can support machines that will be able to love the world, including humans.

#OEB16 opening plenary live blogpost on owning learning

Owning learning: A great session with a range of experts on the topic of current learning problems… and possible solutions. This is a live blogpost.

Tricia Wang (designing for perspectives: the secret for learners to thrive in the 21^st Century)

The individuals are getting too much of the blame. We need to design for perspectives, (@triciawang ).

Sally Ride was the first female astronaut. Once she stopped flying she developed earthKAM, which enables teachers to use the camera on the space station. The students can select the coordinates, and it gives the students a hands-on space experience.

The power of using tech and learning, at best the students really feel science. It introduces a new perspective for students. Video and photography provides participatory options for learning. Technology has seen so many innovations now applied to learning, it is mindblowing. Machine learning is a fancy term to describe what we do with computers, where computers get actions from humans.

Machine learning is a 3;7 billion industry, but what are its limitations? Machine learning still requires human designers and quality data. If the humans overseeing the training are not aware of their own biases, these biases result in the output.

Technology has not increased our understanding of the world. Example: the white interpretation of google photo’s. It is a result of failing to see outside of our perspective.

These technology mishaps happen a lot. Machine biases, Propublica 23 May 2016. (the high risk offender example).

Machines are directing our learning, as such biases in these machines might result in more biases in learning. No one wants these biases to be embedded in machine learning. But the outcomes reveal the limited perspectives of their creators. And this happens easily, perspective collisions happen.

Representing heterogeneity is a difficult challenge. We are still not truly globally connected. The social part is something all of us have a hard time with. Getting a multiplicity perspective is the challenge. There s a lot of confusion, as everyone gets to speak, this means all of that ends up into the social texture of life. So we need to teach people to navigate their own lives through this new social, machine lead system. Perspective shifting, a new form of media literacy, taking into account people that are not like you. This will be one of the most critical skills, but humans need to be trained for this. It is a learning behaviour, so qualitatively learning these skills is possible and should be a priority to enable a global world with true equality.

Relying only on quantitative data, it risks us to be blind by the known. This is why we need people that can actually address these multiple perspectives. To get outside the binary: replace the binary divide with the connected network, to ask different questions (computers replace humans), but why do we not ask what humans can do to work with computers to reduce biases. Always integrate both quantitative AND qualitative data to eliminate the risk of bias. In a pluralistic society, we need this approach.

Look up caroline synders or sinnders… for work with machine learning and ethonographers.

Andreas Schleiger (supporting learners globally to own learning)

The last couple of years we all got experienced at coping with economic crisis. If you look at who found solutions, skills seem to be the key driver to battle inequality or crisis. People at the high end of the skill spectrum see themselves as actors, while the low end sees themselves as objects.

Even today, corporates tell us there are no skilled workers, yet more people are looking for jobs.

So it is about skills and using them, learning them.

How to be ready for social problems, like jobs that will be erased. It is not about robots, just about automation. But augmented reality can bring the real world into any location. Google knows everything, and there is a huge challenge that is coming our way. There is no longer a digital economy. The economy is a digital economy.

People work harder now, then ever before, but the declining levels of productivity is affecting work. There is a growing divide that people with the right skills have less opportunities, thus those without skills have even less options.

The race between technology and skills.

Digital problem solving skills: finding solutions for every day problems. Only 1 in 5 people above 50 years can do this. Even if we look at people 16 – 24 suffer, as only one in two young people can solve every day challenges.

Lots of people are being left outside. The only area where employment grows is the high skills jobs. This is where the economy is quite stable (admittedly, the pay is decreasing for these jobs).

What skills are important: knowledge, integration of different fields of knowledge (think like historian, philosopher, technology… all at the same time will increase your skills and stability). And then looking at details to solve problems via different viewpoints.  

The world rewards for the opposite, for thinking about systems, not the details.

Digital literacy, global literacy… those different perspectives become the challenge.

Skills that matter today is critical thinking, creative thinking. Solving complex problems, social skills, communication…

But something controversial as skills is: resilience, figuring out problems when you cannot see the solutions, curiosity, mindfulness, ethics, courage, leadership, inclusion, empathy. Making judgments becomes more important, that is complex. Self-awareness.

Everything we do reinforces what we did when we were young. If we think of the science changes… it is amazing, we have developed 3D printing, iphones, google maps… you no longer need to teach people something, but skills.

Fundamental success in life: numerical skills (eg data), there is a direct relationship between low skills and declining jobs.

You no longer need to accumulate degrees, but contemporary skills mentioned above.

Literacy skills, learning to learn, cross-sectional skills. We need to teach people these skills to enable them to be able to find the right jobs. 

Roger Schank (who owns learning, not you, maybe AI can help).

This is a person you just need to talk to. The talk will be hilariously invigorating. 

Who owns learning is my question. Everyone but yourself. As the system tells you what to learn, with similar requirements, interpretations of what is best. 

Eliminate testing. The politicians support the testing industry. And forcing testing, forces what teachers need to teach.

Let’s built online learning that does not suck and really teaches us a lot of useful skills. 

Artificial Intelligence: at a certain moment it was put in the freezer due to over-expectation at some point. But now it is again a big business.

At present models human intelligence, but it is not. Schank mentions AI mentor (look up). 

Keynote: non-native English speaking k12 students using MOOCs #MOOC #digitalskills

In January I had the pleasure of being invited to the Institute of Technology in Sligo, Ireland to share my findings on a project which involves high school students using MOOC to increase their digital skills, as well as their practical language skills. This is a project which is running until June 2016, and which spans one academic year. The students are 16 - 17 year olds, and I have been given updates on the project as it evolves. But thanks to the wonderful people at ITSligo, I can also share the keynote on this ongoing project.

In the meantime I did interviews with the students at mid-course. The students have now completed their Group MOOC (having gone through a MOOC they choose in groups of 2 or 3), and are ready to commence the MOOC of their own choice, and engage in it autonomously. The outcomes keep surprising me, as the students clearly pick-up digital skills, increase their digital literacies as they use and take in different media types, and all of this in a non-native language. But  more on the project later. Here is the keynote speech, with slides, and some Q/A. 

Keynote excerpt on 2 Big Data facts impacting #education #learninganalytics #data

About a year ago I was asked to be part of a keynote debate at Online Educa Berlin. The excerpt was part of the keynote debate that is a much loved item at the Online Educa Berlin conference. The idea behind the keynote debate is to discuss in a parliamentary fashion a specific online learning motion. Each panel member can attempt to interrupt the speaker who has the floor, and it is each of the speakers challenge to keep on top of what they want to say, while stopping the other panel members to interrupt.

This keynote was on Big Data and its impact on education; Big data is changing all aspects of society, as Online Educa Berlin is one of the leading eLearning conferences, this debate put forward the motion: "big data is corrupting education". During the keynote debate an argument for or against the motion is made, each time by two speakers. The speakers in this keynote debate were Ellen Wagner, Victor Mayer Schönberger, George Siemens and myself. Together with Ellen Wagner I was supporting the motion.

The full debate can be seen here, if you scroll to the right in the keynote section. OEB offers a wide collection of recorded material from keynote speakers, and it is a treat.

As each speaker only gets 8 - 10 minutes to defend or reject a motion, I decided to focus on two aspects of Big Data impacting education: creating a bigger digital divide, and reproducing the norm. So here is the video of this keynote.