LiveBlog #Ectel2019 Rose Luckin @Knowldgillusion Keynote #AI & #education mindset

 Rose Luckin takes the stage with a headset and immediately getting into her talk. The talk was very informative and to me it looked as though Rose is so knowledgeable about a range of topics, so I got a bit curious and envious in how her mind works [It I heard - I do not know if this is correct, will ask her ] that she only got into academic life later on in life?

Key topic: develop the right AI mindset for businesses

A perfect storm: data mass plus computing power and memory enhancements, sophisticated algorithms ... this made AI part of our lives and education.

3 routes to Impact on Education

• using AI ED to tackle some of the big educational challenges
• education people about AI so that they can use it safely and effectively
• changing education so that we focus on human intelligence and prepare people for an AI world (hardest to do at the moment)

Working with select committee processes to try and take forward new developments. Debating on 4th industrial revolution and what it means that people understand AI (it is not coding, it is about the humans and their understanding of the fundamentals of machine algorithms, awareness, it is a much higher order we need to engage people with).

Need for multidisciplinary teams with equal input
As change happens, we need to change our educational systems (Singapore). Be resilient to change, be adaptive.
The above are not separate routes, it interconnects, and these interconnections increase AI and that we need to change and invest in our society using emerging ideas and realities of these three buckets.
We need to build bridges between communities: all stakeholders (parents, communities, government, coders...).
Currently separated communities need to work together to build a credible, societaly based AI solution.

Companies working with UCL EDUCATE
Not all companies are already using AI, but they want to understand more about it.
EDUCATE was from Europe, but turning into a global program from Jan 2020.
250 educational study start-ups (each start-up has to have a link with London, but they need to have some profile in London, so most UK-originated).
UCL provides training (labs, clinics, blended rooms, mentoring sessions)
It is free for the companies (years spend on figuring out the gaps between educational departments and industry. This was the case for hard sciences and industry, but not education). A lot of the reasons was because they did not know who to talk to, where to start => reason for starting with start-ups, embedding the educational mindset and to understand more about outcomes and validation of educational projects, so what it means when we say 'it works' (complexities... this results in the golden triangle: edtech developers, teachers & learners, academic researchers).

Start-ups are pushed to build a logic model, and the change being the learning that they want to take place. Opportunities they have to analyze the data, how should they demonstrate impact. We hope they will get to the last stage (see picture).
EdWards are set in place (awards to proof evidence applied and evidence aware awards).
120 companies became evidence aware, and 25 become evidence applied (last being much more difficult to achieve).

EDUCATE for schools
objective: build capacity in schools to identify and evaluate edtech that meets the needs of their teaching, learning or environment.
This approach can work in different educational programs.
Sit down, get head teacher in to pick two or three educational challenges - what they find tricky, than teachers are chosen to test it, to find out how the edtech works.
Currently this is under development:
all resources included in option 1, schools identify new or existing edtech to pilot
EDUCATE provides new resources to help schools plan their edtech pilot,
educate povides video and document resurces to walk schools through the pilot process
schools step through piloting process and recieve one hour of 1:1 video mentoring support
evaluate it (not sure I put this in correctly - this last step)

Sources
Century AI:
AI and big data powers personalised learning
Quipper: video insight, smart study planner, knowledge base
EvidenceB KidsCode : paths through materials, optimised parts through material

classic recommender systems (finding the right resources for the educator/student)
Bibblio
teachpitch

Chatterbox: refugee as expert native speaker with matching backgrounds (e.g. engineering background)
OyaLabs cloudbased monitor in the baby lounge and monitors interactions between baby and its cognitive developments for language developments
MyCognition algorithms automatically increase the number of training loops for the domains where you have the greatest need. If attention is your greatest needs you will receive more attention loops, building resilience in attention. As you progress the loops become more challenging. Looks at your attention, actions... assessment and report, which powers aquasnap and takes you to a underwater world (sea routes, fish names...) and adapted to your own cognitive status.

Building an AI mindset
Important for any company that wants to get into AI
What does it means to have the right data,
not just the tech team must understand the data and AI
as an individual it would be good to understand more about AI

Working with OSTC / ZISHI company: example of AI mindset collaboration. What they do: training for trader floors. They have to train everyone. They try to attract diversity in the workforce and pick them from less evident universities. ZISHI tries to use AI, AI for financial sector.
Financial sector has used AI for some time. AI used for assist in recruiting the best traders, assist in training the traders, help traders in improving performance, mentor the traders through out their careers.

Understanding OSTC's performance metrics

• how can training behavior be measured?
• can we profile traders by their trading behavior?
• how do these profiles relate to performance?
• can we then create a tool to help recruitment a tool to help traders and a tool to help managers?

The CEO of OSTC started out at the post floor of Lloyds and moved up. One's he saw the lack of training, he got into training and set up OSTC. Fundamentally what they try to do is creating AI mindset.

Much is not easy or obvious of what traders do

• what others tell me that I do
• what I think I do
• what I really do
• what family thinks you do...

Workflow
Nearly half their traders left less than one year in. So something was wrong, and investment was too costly for the results in the longterm.
Modeling using machine learning techniques to profile traders and make predictions (recruitment data from tests, interviews and videos, trading history data from trading platforms, multimodal data from eye-movements and button clicks, and behavioral data.
Masses of data from the tools used in the company.

Profiling 4 types of traders, using four identified characteristics:
data visualizations, using clustering techniques.
It turns out that the behavioral patterns relate to significantly different performance (risk management, bonuses... and different cognitive abilities & traits (openness to experiences, agreeableness...) [here my mind went off... must be something related to trader-vocabulary?]

Challenges to IA mindset

• collaboration: is everybody onboard?
• getting rid of AI's sci-fi fantasies and fears
• digging in rich soil will bring out stuff. Are we ready to act upon it?
• the appetite comes with the first byte - be ethically prepared to diet
• data is har to collect, standardize, clean, #you-name-it

Opportunities for IA mindset

• map the organisations' data information knowledge wisdom pyramid (and who is where
• identify data sources: what is ready to be picked, what still needs to be ripened or sown
• what can we learn from previous (successful of failed) experiments or pilots? what hypotheses they already have? what are their blind spots?
• metrics - how do we know what success looks like?

OSTC - lessons

• team members across different tiers need to embrace change
• collect as much data 
• tech team in company not the same as data team
• need new expertise to digitize documenten and learning content
• develop coherent and consistent procedures in all offices across the globe despite the cultural bias
• track the daily activities through logs and multimodal data
• develop tools

Developing an AI mindset

• AI is set to transform education
• three core types of interconnected work: using AI, understanding AI, changing education because of AI
• multi-stakeholder collaboration can help achieve these three types of work
• EDUCATE is an example of a multi-stakeholder collaboration to help develop a research mindset in Edtech developers and educators
• for AI companies, or companies who want to use their data and AI we also need to develop an AI mindset (or perhaps initially a data mindset)
• Academic research partners need to be put in this mix

Barclays provided somebody (eagle) in branches, and they would help people to use technology (from simple to complex) to get people engaged about using and thinking about technology, and how they can get involved.

#nanodegree #badges #lifelong learning thoughts

The new Udacity and AT&T initiative rolling out nanodegrees for people who follow specific MOOC or online courses in general has a logical and nice ring to it. The idea is simple: for all learners following a course successfully (= as indicated by the institute/corporation designing the course and its interactions/assignments/tasks), a nanodegree can be earned that illustrate immediate job skills and knowledge. This earning does cost: 200 $ per month (courses on average 6 - 12 months), but it is much cheaper then a college degree. As such Udacity goes for the corporate MOOC creation which (in part) addresses training people for specific jobs through MOOC. Smart move of course from Udacity to corner the more entrepreneurial need job/training market. And although other MOOC providers, such as Coursera do have similar tracks (i.e. specializations), Udacity seems to be a fore runner for this type of MOOCs.

Less discrimination?
A definite bonus of this approach is that companies indicate niches in their workforce, resulting in courses that will indeed develop a capable learner into a potential professional for the job. The fact that the course offered is right on target of the job, also saves time (and costs) for all involved. And, the best learners come out of it based on their actual delivery, so selection based on color, race, ability might fade as a result (yes, utopia). And the fact that nanodegrees are also linked with scholarships to non-profit organizations is a bonus as well. It feels like really good news. In Europe you have entrepreneurial schools popping up, who listen to the industry to find out were there is a gap in the job market and offer courses to alleviate these gaps (iMinds academy is one).

Jobs that will not make it into the future?
So I was hopeful, good initiative to get people into jobs. But then my critical mind set in and suggested some possible downsides.
I can see how many people (increased employment) might enter a contest to get jobs: the best MOOC learners get the position. And those who would go for these jobs - when reading the articles on the nanodegree from NY Times, seem to be the less financially secure. This means there is another divide and conquer tactic being set loose on an already sensitive population. And it would seem to me, that if a job can be learned in a fixed amount of time, it will be one of those jobs that vanish in the near future due to automation, Harold Jarche wrote on the subject of automation on several occasions (but in spite of this critique my other positive brain tells me: yes, but in the meantime people will gain some money in the process, that is good, any income enhances self-esteem, increases economic stability, will lift others as well).

The idea fits in with what Mozilla started using open badges to allow informal certification to be offered when completing certain learning tasks or paths. But then again open badges can be provided for free, and tailored to any need or vision.

Industrial era training approach?
While thinking it over and over again, I cannot seem to shake the feeling that the concept of nanodegree is much more a training following the industrial era (but I can completely miss the ball here, as the courses are still being constructed and might well be creative in their pedagogical approach), but it is the teacher (industry) who offer a fixed set of content and tasks (cfr old books and assignments) to learn to pass one specific set of tasks. So although this is indeed a new approach, purely on the basis of the corporate/job specific angle, it does sound like old school pedagogy).

Concern added by Rebecca Hogue on shift employers need to pay their training
After reading this blogpost Rebecca added a relevant remark: "One issue I see with this model is that this specialized industry specific training used to be paid for by companies. It used to be that you would be hired for your general skills, and the company would pay you to be trained in the specific skills that they needed. What I see here is an opportunity for the companies to stop paying to have their employees trained in the specific skills they need, and push that cost of business to the employees themselves". 
And those employees would not even be sure that they would actually be employed, even after attempting (and paying) several nanodegrees out of their own pocket. Thanks Rebecca!

Creativity and replacing more expensive workers?
From an economic point of view, there is this automation risk. Though I admit, first nanodegrees seem to have an openness towards creativity as front-end/back-end web developer come into mind, but of course not sure what is really meant by that or how open/creative this is envisioned. But there is more, once these types of courses become more mainstream, they might result in a discrimination of those who cannot afford the courses, and thus getting into a more difficult predicament.

Older workforce?
The first nanodegrees seem to target entry-level jobs, but there might be strategies being worked out for higher level jobs as well (digital skills, networked professionals). What about older workers? Is there a risk of more experienced workers being replaced by on-the-job-focused-less-expensive workers? If so, how to deal with that?

Copying societal global North as model?
And, if the jobs are provided by profit, then they are part of a chosen, institutionalized societal pattern, which always risks increasing those digital divides that already exist in that society. Additionally, it is a model coming from the global North, which means it has a colonial ring to it, and potentially transforms those norms and ways of working over onto other areas on the globe. All of us need to work for those, in jobs that are existing (yet possibly be extinct through automation soon).

Do I like the concept of nanodegrees: of course! but it is always nice to ponder, reflect and try and figure out potential pitfalls as well.