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)
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)
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.
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...
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.