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Pushing the boundaries of Artificial Intelligence - CILIP: the library and information association

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Open access - are we almost there for REF?

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Open access - are we almost there for REF?

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Author: David Sweeney

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A report published today by Research England (on behalf of the research councils, Jisc and Wellcome) shows that UK universities are working hard to ensure that they are compliant with funders' open access policies. The support from authors, professional services staff and academic libraries has been crucial in implementing open access and ensuring that research outputs from UK universities are shared as widely and as freely as possible.

The report is available here from Research England.

Access for all - but at a cost

According to a report published by Universities UK in last year, thirty-seven per cent of UK outputs are freely available to the world immediately on publication through either "gold" or "green" open access. This figure rises to fifty-three per cent after 24 months. Against a global average of twenty-five per cent we are doing well - but it has come at a cost.

With rising subscription charges and increasing article processing charges (APCs) we need to question whether or not the UK has delivered a cost-effective way of achieving open access.

There are other roads for us to travel down, as we are seeing from our bolder colleagues in Germany, France and Sweden. Germany's nationwide negotiation coalition, Projekt DEAL, refused to renew Elsevier's contract in 2017 and is thought to be saving more than €10 million (£8.7 million) a year in journal subscription fees. Likewise, France's Le Consortium Couperin has suggested that research institutions are saving millions of euros in subscription costs after refusing to agree a new deal with Springer.

Sweden are the latest to take this route with the Bibsam Consortium choosing not to renew their agreement with Elsevier for the first time in twenty years.

Fair publishing

In order to us to move forward with open access there must be a fair and sustainable price model that truly reflects the costs involved in publishing and not, as was signalled in Springer-Nature's IPO document, what the market will stand based on the brand value of the journal. We should encourage sustainable and transparent costing on the direct and indirect costs of publishing open access. This is already being done by some publishers - for example, Ubiquity Press offers a transparent breakdown of how their publication fees are calculated, in order to establish trust with authors, institutions and funders. This model allows the journal to be completely sustainable based on APCs alone.

Coordinated engagement and negotiations with scientific publishers to improve contractual conditions and secure greater transparency on costs is a national priority for open access to research publications. The UK universities' negotiating body, Jisc Collections, should be able to open negotiations with publishers with support from funders and the university constituency. HEIs have an interest in the outcomes of these negotiations as both customers and suppliers of research outputs. Funders and universities are partners in this endeavour, recognising the imperative to work together and co-ordinate policy.

An internal UKRI review

UKRI is about to embark on an internal open access review. Ensuring that the findings of publicly-funded research are freely accessible and discoverable online is central to UKRI's ambitions to accelerate research and innovation in the UK. This is an opportunity to take a good look at what is working well at the moment - and what isn't.

The appointment of Robert-Jans Smits as the EU's special envoy on open access will also see the OA agenda pushed forward. He has been tasked to make all publicly funded research freely available by 2020 - by no means an easy feat, but one that relies on working with stakeholders to build an open access future.

Collectively we need to ensure that open access is delivering what it sets out to achieve, and that universities and researchers are receiving value for money to access new scientific outputs. UK Research and Innovation will work with other funders and purchasing groups to achieve that.

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Open access - are we almost there for REF? | Wonkhe | Comment

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A report published today by Research England (on behalf of the research councils, Jisc and Wellcome) shows that UK universities are working hard to ensure that they are compliant with funders' open access policies.

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Select committee

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Committee Chairman

The Chairman of the Committee, Lord Clement-Jones, said:

"The UK has a unique opportunity to shape AI positively for the public's benefit and to lead the international community in AI's ethical development, rather than passively accept its consequences.

"The UK contains leading AI companies, a dynamic academic research culture, and a vigorous start-up ecosystem as well as a host of legal, ethical, financial and linguistic strengths. We should make the most of this environment, but it is essential that ethics take centre stage in AI's development and use.

"AI is not without its risks and the adoption of the principles proposed by the Committee will help to mitigate these. An ethical approach ensures the public trusts this technology and sees the benefits of using it. It will also prepare them to challenge its misuse.

"We want to make sure that this country remains a cutting-edge place to research and develop this exciting technology. However, start-ups can struggle to scale up on their own. Our recommendations for a growth fund for SMEs and changes to the immigration system will help to do this.

"We've asked whether the UK is ready willing and able to take advantage of AI. With our recommendations, it will be."

AI Code

One of the recommendations of the report is for a cross-sector AI Code to be established, which can be adopted nationally, and internationally. The Committee's suggested five principles for such a code are:

1. Artificial intelligence should be developed for the common good and benefit of humanity.

2. Artificial intelligence should operate on principles of intelligibility and fairness.

3. Artificial intelligence should not be used to diminish the data rights or privacy of individuals, families or communities.

4. All citizens should have the right to be educated to enable them to flourish mentally, emotionally and economically alongside artificial intelligence.

5. The autonomous power to hurt, destroy or deceive human beings should never be vested in artificial intelligence.

Recommendations included in the report

Other conclusions from the report include:

Many jobs will be enhanced by AI, many will disappear and many new, as yet unknown jobs, will be created. Significant Government investment in skills and training will be necessary to mitigate the negative effects of AI. Retraining will become a lifelong necessity.

Individuals need to be able to have greater personal control over their data, and the way in which it is used. The ways in which data is gathered and accessed needs to change, so that everyone can have fair and reasonable access to data, while citizens and consumers can protect their privacy and personal agency. This means using established concepts, such as open data, ethics advisory boards and data protection legislation, and developing new frameworks and mechanisms, such as data portability and data trusts.

The monopolisation of data by big technology companies must be avoided, and greater competition is required. The Government, with the Competition and Markets Authority, must review the use of data by large technology companies operating in the UK.

The prejudices of the past must not be unwittingly built into automated systems. The Government should incentivise the development of new approaches to the auditing of datasets used in AI, and also to encourage greater diversity in the training and recruitment of AI specialists.

Transparency in AI is needed. The industry, through the AI Council, should establish a voluntary mechanism to inform consumers when AI is being used to make significant or sensitive decisions.

At earlier stages of education, children need to be adequately prepared for working with, and using, AI. The ethical design and use of AI should become an integral part of the curriculum.

The Government should be bold and use targeted procurement to provide a boost to AI development and deployment. It could encourage the development of solutions to public policy challenges through speculative investment. There have been impressive advances in AI for healthcare, which the NHS should capitalise on.

It is not currently clear whether existing liability law will be sufficient when AI systems malfunction or cause harm to users, and clarity in this area is needed. The Committee recommend that the Law Commission investigate this issue.

The Government needs to draw up a national policy framework, in lockstep with the Industrial Strategy, to ensure the coordination and successful delivery of AI policy in the UK.

Further information

Image: iStockphoto

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UK can lead the way on ethical AI, says Lords Committee - News from Parliament

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The Chairman of the Committee, Lord Clement-Jones, said: "The UK has a unique opportunity to shape AI positively for the public's benefit and to lead the international community in AI's ethical development, rather than passively accept its consequences.

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Lords AI committee

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Many of the hopes and the fears presently associated with AI are out of step with reality. The public and policymakers alike have a responsibility to understand the capabilities and limitations of this technology as it becomes an increasing part of our daily lives. This will require an awareness of when and where this technology is being deployed.

Access to large quantities of data is one of the factors fuelling the current AI boom. The ways in which data is gathered and accessed need to be reconsidered, so that innovative companies, big and small, have fair and reasonable access to data, while citizens and consumers can also protect their privacy and personal agency in this changing world.

Large companies which have control over vast quantities of data must be prevented from becoming overly powerful within this landscape. We call on the Government, with the Competition and Markets Authority, to review proactively the use and potential monopolisation of data by big technology companies operating in the UK.

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The UK is in a strong position to be among the world leaders in the development of artificial intelligence. It contains leading AI companies, a dynamic academic research culture, a vigorous start-up ecosystem and a constellation of legal, ethical, financial and linguistic strengths. Artificial intelligence,

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The fourth industrial revolution: how can universities respond to the rise of the robots?

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The fourth industrial revolution: how can universities respond to the rise of the robots?

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Author: Martin Hamilton

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It seems that everywhere you look these days there are articles about . It's certainly the matter of the moment, but how can we best prepare our learners for the new careers that are being ushered in by these technologies? Moreover, how do universities need to change, and should we be concerned?

Dame Wendy Hall's 2017 review of artificial intelligence concluded that "industry should sponsor a major programme of students to pursue masters' courses in , with an initial cohort of 300 students".

Further recommendations include creating an additional 200 PhD places dedicated to at leading universities, and the recently published Industrial Strategy white paper earmarks £30m funding to test the use of and innovative education technology in online courses.

What will future courses look like?

We can learn a lot from the Stanford online education spinout Udacity, which launched a self-driving car engineering nanodegree in autumn 2016.

This short, $2,400 course is roughly equivalent to a masters' degree and aims to take learners through key aspects of using to process images and sensor data from a self-driving car. This is one of the most challenging applications for , because real-world road conditions can be so unpredictable.

The nanodegree demonstrates how different courses could be to traditional graduate or postgraduate courses.

Perhaps most significantly, there are no academic institutions involved in this course - learners get assigned industry mentors

The course is largely self-paced learning from online material and is only several months long. Perhaps most significantly, there are no academic institutions involved in this course - learners get assigned industry mentors from the likes of Mercedes, BMW and Uber.

A wake up call

The real wake-up call for our universities is that, almost overnight, Udacity had over 30,000 people from all over the world wanting to take this course.

Could traditional degrees learn from the nanodegree's flexibility and fast pace? It seems that's what students want.

22,000 students have told us recently that they want staff to be better with digital, not use more of it

At Jisc, 22,000 students have told us recently that they want staff to be better with digital, not use more of it, and that they strongly value the convenience of online systems.

How could institutions replace people with ?

Robotics and are also set to have a transformational effect on the business of being a higher education institution, too.

San Francisco startup Knightscope has been in the news recently after one of its robots was apparently being used by a customer to discourage homeless people from sheltering near their premises. Will we see our universities and colleges replacing their security guards and manual labour roles with robots?

Could machines eventually sweep dorms, serve pints in the SU, or even do the day-to-day organisational admin?

This might seem farfetched, but Knightscope has priced its robots very competitively. At just $7 an hour, they're far cheaper than hiring a security guard and the robots work all day and night with just the occasional break for charging. Could machines eventually sweep dorms, serve pints in the SU, or even do the day-to-day organisational admin?

We've already seen some signs of institutions using in a targeted way to help students, such as when Ashok Goel, a Professor at Georgia Institute of Technology introduced his new assistant Jill Watson, who would field questions from students to reduce his workload.

Yes, you've guessed it, Jill was actually an 'bot' using the award-winning Watson software from IBM.

Using to assist learning

Closer to home, at Jisc we have worked with around 100 institutions to develop a national learning analytics service that uses to:

Improve retention

Enhance the student experience

Make the organisation itself more productive

The service uses the data from institutional IT systems like the library catalogue and virtual learning environment, which would historically have been discarded, but can actually give us some very useful indicators of student engagement over time and correlation with learning outcomes.

The future

Universities are a bit like ocean liners - they tend to struggle with sudden course changes.

It's clear that institutional agility is becoming an increasingly important topic

In my job as a Futurist I often work with senior leadership teams that are devising or implementing a digital strategy, and it's clear that institutional agility is becoming an increasingly important topic. This may involve new ways of working such as fully online delivery and blended learning, but just keeping track of key technologies can be quite a challenge as they keep evolving at a very fast pace.

So yes, the robot revolution is coming, and things won't ever be the same again.

The jobs sector will change, and so then, must universities. If institutions keep up, our students will be prepared for the robotic future ahead. Technology doesn't wait for anyone, so we may as well jump on board. It's going to be a wild ride!

Tags:

Accessibility

Assessment

Data and analytics

Digital literacy

Infrastructure

Organisational change

About the author

Futurist, Jisc

I'm Jisc's resident futurist. My job is to keep an eye open for emerging trends and new technologies, and see what we can do across the FE, HE and skills sector to exploit and embrace them - or mitigate against them.

Prior to joining Jisc I set up a supercomputer centre for the Engineering and Physical Sciences Research Council ( EPSRC), and have a particular interest in how we can share these kinds of capital intensive facilities between institutions and also with industry.

07971 989487

@martin_hamilton

martin.hamilton@jisc.ac.uk

# data[].attributes.attachment_link.title
The fourth industrial revolution: how can universities respond to the rise of the robots?

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It seems that everywhere you look these days there are articles about . It's certainly the matter of the moment, but how can we best prepare our learners for the new careers that are being ushered in by these technologies? Moreover, how do universities need to change, and should we be concerned?

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Author: James Clay

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What is the future of the library? This session at the CILIP Conference will explore the potential technologies and the possibilities that can arise from the d...

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Putting the learner at the centre (artificial intelligence)

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Putting the learner at the centre (artificial intelligence)

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Speaker: Rose Luckin

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Mapping the future of academic libraries

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Mapping the future of academic libraries

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Authors: Stephen Pinfield; Andrew M Cox; and Sophie Rutter.

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Author: Martin Hamilton

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Introduction

When we think about artificial intelligence () we tend to picture the anthropomorphic robots of classic science fiction stories. In an education context, perhaps it's a robot teacher gliding silently down a school corridor, some time in the future. But is all around us now, often as an invisible addition to apps and services that we use every day.

In this report I'll take a look at how can and is being used in education, from recognising and classifying data to generating new content from scratch. I'll also look at how can be cheated and even defeated by a determined adversary, and consider how we can ensure that an ethical approach is taken to using in education.

Artificial intelligence in a nutshell

has been in the headlines a lot in the last couple of years - perhaps most notably thanks to the work of Google's DeepMind division. DeepMind famously produced the world-beating Alpha Go , and the underlying TensorFlow software used by DeepMind has become very widely adopted after Google made the code 'open source'. This has already had a virtuous circle effect for Google, with most developers cutting their teeth on TensorFlow projects, and the software has quickly become established as the de facto standard for .

But has actually been around for a lot longer - the precursors of today's techniques date back to research from the 1960s, with the advent of genetic algorithms and neural networks.

for classification - from self-driving cars to learning analytics

Google's DeepMind artificial intelligence beat the (human) world champion at Go by building predictive models based on learning from thousands of previous games. Could a similar approach help teachers, learners and institutions to continuously adapt to improve retention and learning outcomes? Could an one day generate a personalised curriculum for learners in a similar way to the generative art produced by Facebook's Eyescream or Google's Deep Dream research projects?

With 90% of 16-21 year olds in the UK owning a smartphone, today's learners are constantly generating a 'digital exhaust' of data. It's not unreasonable to think that some of that data could potentially be used to improve teaching and learning outcomes. But when we start to make predictions based on linking data that the institution holds with learners' own data, a whole new class of ethical issues arise. Do you know where your data is, and where it's going?

With 90% of 16-21 year olds in the UK owning a smartphone, today's learners are constantly generating a 'digital exhaust' of data.

It's fascinating to see how is used in self-driving cars, and this perhaps gives us some clues as to how it could be used in education. Self-driving cars are typically studded with camera, ultrasonic sensors and LIDAR systems which send out millions of laser beams per second to map the environment around the car.

In an education context some of the sensors may well be (CCTV) cameras, but our institutions are already kitted out with a wide range of IT systems that know who is using them, when, and (to an extent) what for.

Consider for example the virtual learning environment or learning management system, library systems, attendance monitoring systems and so on. At the moment the data held in these systems is typically standalone, and often consists of diagnostic log files that are likely to be deleted without being processed in any way.

This digital exhaust could tell us a great deal about how engaged a learner is, and when we start to link datasets together we can potentially build up quite a nuanced picture of when and where learning takes place. This is what we are starting to do now with predictive analytics as part of the Jisc learning analytics service, which has been co-created with a community of around 100 institutions and vendors.

Taking this example a little further, the Jisc-supported Learnometer startup has built an inexpensive hardware monitoring device to go into classrooms and lecture theatres which monitors a range of environmental parameters from noise and light levels to temperature, humidity and pollution.

As we start to look at linking learning outcomes, timetabling systems and Learnometer data, we may well find a correlation between poor learning outcomes and rooms that are simply unsuitable for teaching. One school in Dubai that has been piloting Learnometer has already changed over 160 rooms in response to the data from the system.

But has a wide range of other potential applications in education - perhaps the most notable being adaptive learning, where the curriculum is modified dynamically in response to the learner's strengths and weaknesses. In this case we could view the as a coach or mentor, identifying areas where the student could potentially improve their performance.

AIs are getting much smarter, and learning how to create new digital content all by themselves.

But AIs are getting much smarter, and learning how to create new digital content all by themselves. This 'generative' generally works by pitting two neural networks against each other - a generator, and a classifier. The generator produces some output based on an initial data source, which could even be white noise, and then passes it to the classifier for scoring. Let's picture that you are the generator, trying to create a photo realistic picture of a cat - the more cat-like your picture, the higher your score.

This simple feedback mechanism turns out to be highly effective at creating pictures, videos and even text that are a good match for some starting reference point. This artistic style transfer technique has been widely adopted to implement advanced photo filters in social media apps. Microsoft have taken this approach one step further by creating an AI that generates a picture based on text.

Generative clearly has a lot of potential in the sort of personalised learning scenarios discussed above - wouldn't it be great if the lecturer didn't have to map out all of the possible pathways through the material and devise the reinforcement exercises in each area?

However generative also raises all sorts of interesting questions about cheating. Rather than simply downloading someone else's attempt to answer an essay question, a generative could adapt their answer to the style of the learner who was attempting to cheat. Perhaps this will lead to an arms race, with both sides deploying ever more advanced AIs to cheat and to detect cheating.

Generative AI is particularly interesting in art and design, where it's easy to see it simply becoming another tool available to practitioners. We often talk about the need to develop digital skills and digital capability in our institutions and the general population, and this could be a very stark example of an area where some quite deep technical concepts need to be assimilated in order to achieve meaningful results. We could ask ourselves here how far down the rabbit hole a student or teacher might expect to go.

In a few years' time it may be commonplace to find art and design students doing Python programming to train a TensorFlow model. This might seem unlikely at present, but we should keep in mind that Python is one of the programming languages that today's schoolchildren will typically encounter as they study computing. TensorFlow even works reasonably well even on the humble Raspberry Pi computer which has proven popular in classrooms across the world, as I show in the video demo below.

Adversarial objects - a shock to the system

We tend to assume that the uses people will put technology to are largely benign, if not socially productive in themselves. But that isn't always the case, as we've seen from the plagiarism example above. What happens when someone is actively trying to cheat the which controls or monitors some critical institutional process?

This might seem hard to do, but we've recently seen some quite alarming examples of small perturbations in data that are enough to trick machine learning models into mis-classifying the data they're fed with. One particularly notable example is the work of the labsix student team at MIT, which has shown how small peturbations in the inputs to a neural network can lead it to mis-classify objects. Labsix's examples include a 3D printed turtle identified by the neural network as a rifle, a photo of a tabby cat identified as guacamole, and a baseball that the neural network thinks is an espresso.

some element of human interaction is essential in any cases where there are consequences for those involved.

However there is a deeper implication here that we need to keep sight of - processes that are entirely automated are inherently highly vulnerable to adversarial interference, and some element of human interaction is essential in any cases where there are consequences for those involved.

Even now we are seeing generative AI being used to create fake photo and video imagery, to implicate innocent victims in crimes, for social shaming, and other nefarious purposes. Imagine a situation where the is both judge and jury, and the potential for disaster becomes clear.

Conclusions

We've talked a lot in this report about artificial intelligence, but the truth is that none of these computer programs is remotely intelligent in the way that a human being is. There is no sentience here, even if a digital assistant like Amazon Alexa or Apple's Siri can on occasion give a passable pretence at this. The computer is simply following a script, and using to help it recognise cues that will trigger particular parts of that script.

Like the self-driving car breaking sharply to avoid a cat running out into the road, AIs in education have the potential to be constantly sampling far more data points than any human being ever could. They can also instantly marry linked data from disparate sources to help steer both learners and teachers towards the best possible outcomes. However, as we've seen AIs are still quite immature and somewhat vulnerable to bad actors that might want to distort results for nefarious purposes - or simply for laughs.

ethics need to be at the forefront of any projects to use in education.

This is why ethics need to be at the forefront of any projects to use in education. We need to be clear what data is being processed, where, and how. And any actions or recommendations made by an need to be subject to human review. Even then there is one final issue that we have to face - AIs essentially evolve the computer program that drives them, which can make it hard to identify why an made a particular decision. If we could ask the self-driving car why it swerved, the best that it might be able to say is that 'something 75% catlike jumped out in front of me'. In much more nuanced situations such as teaching and learning we may find that the moves in mysterious ways.

Further reading

Be sure to read our other horizon scanning reports and keep a look out for new material. Finally, do let us know what you think of this report by leaving a comment or contacting me.

Tags:

Content

Data and analytics

About the author

Futurist, Jisc

I'm Jisc's resident futurist. My job is to keep an eye open for emerging trends and new technologies, and see what we can do across the FE, HE and skills sector to exploit and embrace them - or mitigate against them.

Prior to joining Jisc I set up a supercomputer centre for the Engineering and Physical Sciences Research Council ( EPSRC), and have a particular interest in how we can share these kinds of capital intensive facilities between institutions and also with industry.

07971 989487

@martin_hamilton

martin.hamilton@jisc.ac.uk

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When we think about artificial intelligence () we tend to picture the anthropomorphic robots of classic science fiction stories. In an education context, perhaps it's a robot teacher gliding silently down a school corridor, some time in the future. But is all around us now, often as an invisible addition to apps and services that we use every day.

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The future of data-driven decision-making

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The future of data-driven decision-making

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Author: Martin Hamilton

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How can we build on recent initiatives in areas like business intelligence and learning analytics to help the UK to move to a culture of data led decision-making in research and education - taking a leaf from the likes of Amazon and Google?

At Jisc we have just launched the second in our series of Horizon Scan reports, looking at the future of data-driven decision-making.

The report introduces the work that Jisc and partners including Hesa have undertaken in this area, and highlights several institutional success stories. It concludes by identifying "three big ideas" that we at Jisc believe would help the sector to move forward in taking a measured approach to achieving the full potential of the data that it holds.

I hope you find the report useful - and I have to stress that we view this as the beginning of a dialogue. We would love to hear your thoughts on this increasingly vital topic, so please do send us feed back - either directly to the authors, or via the #jiscfutures hashtag on social media.

Posted by on in Future of the Organization, Report.

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Report: The future of data-driven decision-making

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How can we build on recent initiatives in areas like business intelligence and learning analytics to help the UK to move to a culture of data led decision-making in research and education - taking a leaf from the likes of Amazon and Google?

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Higher Education's Top 10 Strategic Technologies and Trends for 2018

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Higher Education's Top 10 Strategic Technologies and Trends for 2018

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Report authors: Susan Grajek; and Joanna Grama

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Author: Martin Hamilton

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The final published version of this report is now available from the Jisc website
TL;DR: This is a short report that looks at the potential in research and education of blockchain, the technology that underpins the Bitcoin virtual currency. Blockchain is currently at the peak of the hype cycle, so I spend a little time looking at how we can identify what's realistic and avoid inflated expectations.

The early history of Bitcoin is worth reflecting on:

2007: "Satoshi Nakamoto" allegedly begins development of the Bitcoin virtual currency

2009: Version 0.1 of Bitcoin is released on SourceForge, and the first transaction takes place

2010: Programmer buys pizza for 10,000BTC(now worth $39,454,600!)

2011: Silk Road "dark web" marketplace opens

2011: 25% of the 21 million possible Bitcoins now generated through mining operations

More at http://historyofbitcoin.org/

Let's unpick this a little - Bitcoins are generated from thin air by computers solving complex mathematical puzzles, a process known as "mining", and there are a finite number of them. Not only this, but the puzzles get more complicated as more Bitcoins are mined.

Early Bitcoin mining could be done on a conventional PC, but it quickly became necessary to use very powerful hardware, and most Bitcoin mining operations these days involve hundreds or thousands of high performance servers in their own dedicated rooms or even buildings.

Blockchain is the underlying "distributed ledger" technology that powers Bitcoin. Audrey Watters has written this very helpful summary of blockchain on her Hack Education site:

"The blockchain is a distributed database that provides an unalterable, (semi-)public record of digital transactions.

Each block aggregates a timestamped batch of transactionsto be included in the ledger - or rather, in the blockchain.

Each block is identified by a cryptographic signature.

These blocks are all back-linked; that is, they refer to thesignature of the previous block in the chain, and that chaincan be traced all the way back to the very first block created.

As such, the blockchain contains an un-editable record of all the transactions made."

It's worth reflecting for a moment on the key concepts here - an unalterable, semi-public database, that multiple untrusted parties can add entries to. Many early Bitcoin proponents saw it as an alternative to fiat currency controlled by governments and central banks. Some of these people were coming from a Libertarian background and generally suspicious of big government and central control, but others had genuinely nefarious intentions such as selling illegal goods online. We'll see as the report goes on that whilst this might be where blockchain is coming from, its ultimate destination could be a very different place indeed, due to it having been embraced by those self-same governments and bankers - e.g. the UK government's recent blockchain welfare payments trial.

We may never know who Satoshi Nakamoto really is or was, but making Bitcoin (and by extension blockchain) open source turned out to be a master stroke - a community quickly sprang up around the technology, working to improve it and fix bugs, and build products and services that integrated with it.

One characteristic of open source software is that people can "fork" it to create their own versions, or even base whole new products on it. What happened initially with Bitcoin was that a number of competing virtual currencies appeared, including Dogecoin, perhaps the world's first joke currency.

A little further down the line people started to realise that the underlying technology could be useful in a wide range of applications. A notable example here is Namecoin, the first Bitcoin fork. Namecoin provides a decentralised alternative to key Internet infrastructure such as domain names and digital identifies

Whilst the blockchain concept started life as the underpinnings of a virtual currency, this interest in more general application quickly grew, leading to the development of Ethereum, a general purpose programmable blockchain system. Ethereum extends the blockchain concept to support "abstract decentralised transfer of value to a generalised state transition function, supporting any application" using a mechanism called smart contracts.

Smart contracts are small chunks of computer code written in a language that looks very similar to every web developer's favourite (or bug bear?) JavaScript. This might sound intimidating but essentially what they do is automate the "if this, then do that..." elements of existing decision-making processes. For example: when the incoming funds have successfully cleared, record that the house ownership has transferred to the purchaser.

Using blockchain in research & education

We'll look at three key areas where blockchain could play a useful role, and some examples of projects and services that are exploring these use cases.
2.1 Credentialling
The Blockcerts project from MIT and their collaborators has developed a blockchain based platform that lets institutions "build apps that issue and verify blockchain-based certificates for academic credentials, professional certifications, workforce development, and civic records".

Blockcerts isn't the only show in town, though. There are several other companies and projects exploring blockchain based approaches to issuing and verifying credentials, including startups such as Sweden's GradBase and the UK's APPII.

How could this work in practice? Let's picture a few examples:

Is Martin really a medical doctor? Should I let him perform brain surgery on me?

Martin wants to transfer course to another university and take his academic credit with him

Martin has been doing a work placement and his employer has some feedback to add

Martin has lost his A Level / degree certificates...

2.2 Research provenance and reproducibility
The Blockchain for Science project is developing a manifesto that looks at how blockchain could "reduce overhead and accelerate the scientific process", for example:

Where is the code and data linked to Martin's paper?

Who has cited Martin's paper / code / data?

Who has re-used Martin's code and data?

What grants has Martin brought in?

What collaborators and what equipment has Martin been working with?

Has Martin published in enough prestigious journals to get a bonus / tenure?

2.3 Efficiency, effectiveness and social value
"Blockchain provides a new way for organisations to engage with technology to reduce cost, improve speed and transparency and integrate social value" - Nick Petford, University of Northampton VC & CEO (read Nick's article on blockchain for procurement in Efficiency Exchange)

How could this work? Let's look at some examples:

Martin's student loan is automatically approved and tuition fees paid on confirmation of his place

Martin's institution tracks provenance of goods and services, e.g. through a partnership like Project Provenance or the Blockchain Alliance for Good

Martin's library fine is automatically deducted from his "eduCoin" account

Martin decides to switch to a course at another institution - credit transfer takes place automatically

A lot of people are exploring whether blockchain could help their project or service to work more efficiently, or looking at creating something which is wholly new and powered by blockchain. Whilst there are undoubtedly huge opportunities, there are also a number of traps for the unwary. Coin Sciences CEO Gideon Greenspan has written a great article on avoiding the pointless blockchain project, which I have summarised in the following bullet points.

In short, your proposed use of blockchain:

Must be a database - think in terms of adding new transactions and reading the history of previous transactions. It's not a file store!

Must have multiple writers / updaters - if there's only one updater, you could just use a conventional database

You don't trust the folk updating the database - if you do trust them, you could just use a conventional database and grant them access

You don't need a trusted intermediary to vouch for updates / updaters - i.e. updates are permissionless. If you rely on a trusted intermediary then you could just a conventional database and grant them access

Transactions are often dependent on each other - this is how the true potential of blockchain is unlocked, e.g. think about degree certificates only being released on successful completion of exams and coursework + payment of outstanding fees and fines

Database contains rules for assessing the legitimacy of transactions - this is the underlying principle for Ethereum's smart contracts

Database contains a mechanism for conflict resolution -relying on external data or services for this can be problematic as any external interfaces need to produce the same results no matter which blockchain server node is contacting them, and some operations (like several of the steps involved in buying a house) can't inherently be repeated by multiple blockchain nodes without unwanted consequences

Information / asset in database can be drawn down - i.e. who stands behind the information represented on the blockchain, for example a funds transfer, an exam board or university, government property or land registry

You will also recall the key blockchain concepts that Audrey Watters introduced - an unalterable, semi-public database, that multiple untrusted parties can add entries to. As part of the embracing of blockchain by Wall Street and central government, a number of these pillars are being chipped away at. For example, Accenture recently won a patent for their work on making the blockchain editable using the wonderfully named "chameleon hash".

Whilst the Bitcoin blockchain is inherently a public record of transactions, a number of companies are now offering private blockchains, and "permissioned" blockchains that only certain entities can update. Whilst these developments are sometimes frowned upon by blockchain pioneers, it may well be that they provide the certainty and oversight that is required to make blockchain truly mainstream.

There are a number of issues with Bitcoin derivatives that make mass adoption problematic - in particular the requirement for an underlying virtual currency which must be mined using a compute (and hence power/cooling) intensive process. Alternatives to this "proof of work" approach are starting to emerge in next generation distributed ledger technologies like the Hyperledger system which underpins Sovrin self-sovereign digital identities.

Transaction time itself can also be a huge problem - Bitcoin is currently only able to process around 4 transactions/second, with Ethereum somewhat faster at around 20 transactions/second. This compares unfavourably with PayPal at 200 transactions/second. For us to be able to use virtual currencies like Bitcoin and the underlying blockchain technology all across our daily lives then performance will have to be significantly improved. This is an active research topic at present.

Finally, while we having been talking about blockchain as a "database", it isn't suitable for storing documents or digital media onto, because each blockchain server node has to store a copy of the entire blockchain. Blockchain applications typically manipulate references to data held elsewhere, such as Digital Object Identifiers for research papers. It should be noted that there are also parallel efforts to create distributed file storage systems, like the InterPlanetary File System (IPFS).
Conclusions
There are plenty of potentially transformational applications for blockchain or distributed ledgers more generally in research and education. However, we are still at the "28k modem" stage, comparable with the early web of the 1990s, and there are many traps for the unwary.

Most importantly, take care to avoid the pointless blockchain project - right now there is a lot of "blockchain washing" to make an unattractive project seem more appealing. If in doubt, use Gideon Greenspan's test to help you understand whether blockchain is truly necessary, or simply adds an extra layer of complexity and new failure modes.

Potential collaborators around blockchain in research and education are global as well as local. In this day and age we can collaborate on a global scale around digital technologies just as easily as we can with our neighbours, so we can all drive on the same side of the road and there's no need to have the equivalent of our own special UK electrical plugs and sockets! And there are a large number of open source Ethereum applications that you can build on.

If having read this report you are interested in pursuing blockchain further, we'd love to talk to you - so please do get in touch. You might also want take a look at the presentations at the recent Blockchain in Education conference, and this report from the Joint Research Centre of the European Commission, which are a good round up of the state of the art right now.

We are collaborating with the Open University on their Open Blockchain R&D project, which has set up a testbed blockchain network for modelling research and education use cases.

Finally, do let us know what you think of this report by leaving a comment. be sure to read our other horizon scanning reports and keep a look out for new material - we aim to put a new report out every couple of months.
About Jisc
We are a not-for-profit company owned by and serving the HE, FE and skills sectors in the UK. There are around 18 million users of our shared services like the Janet network, eduroam, JiscMail, and our shared data centres. We also do a number of national deals with IT vendors and publishers, such as Amazon, Google, Microsoft, Elsevier and Springer. Our other key activity is to provide advice & guidance to the sector on digital technologies in research and education.

My role at Jisc is to lead a small group which is exploring the potential in research and education of emerging technologies like Virtual and Augmented Reality, machine learning and brain computer interfaces. We help institutions to devise and implement their digital strategies, and to build the evidence base around new technologies.

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Report: Blockchain in research and education

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The final published version of this report is now available from the Jisc website TL;DR: This is a short report that looks at the potential in research and education of blockchain, the technology that underpins the Bitcoin virtual currency.

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The Digital Book Thief has a Napster Moment - Edinburgh Near Future Library Symposium

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The Digital Book Thief has a Napster Moment - Edinburgh Near Future Library Symposium

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Author: Martin Hamilton

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The Digital Book Thief has a Napster Moment - Edinburgh Near Future Library Symposium

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We've grown accustomed to being able to call up any information, anywhere, any time - but what happens when our digital landlord forecloses? When that service ...

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Student wellbeing on an intelligent campus

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Student wellbeing on an intelligent campus

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Author: James Clay

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What's the issue?

As well as enabling a student to achieve their academic goals, colleges and universities have a responsibility for a student's wellbeing. Their health, both physical and mental, security and safety, are all important to institutions. The development of intelligent campuses can offer considerable support for student wellbeing.

Safety and security

The intelligent campus is a safer and more secure campus. For examples intelligent street lighting is being developed as part of Future City Glasgow initiative. Street lighting will dim during quiet periods but the use of sensors to monitor movement and noise can bring up the lighting. The lighting could even be controlled by the approach of students via a signal from their mobile device.

A range of apps are already available to help students to be safe at night. A typical example is the Circle of Six app which keeps an individual in touch with their 6 most trusted contacts making it very easy to send help messages with their location coordinates. Apps could be integrated into the intelligent campus to provide help beyond their friends when a student is lost, worried, in trouble or in a dangerous situation. An example is GuardianSentral which works with campus security and safety systems such as emergency phones and campus police. It provides GPS tracking of individuals who are in danger. It can provide check-in facilities and an arrival time at a destination. A security alert is sent if the student does not check-in. If the student is feeling uneasy the app can ask campus police to monitor them via GPS or send an officer to escort them. If they are in immediate danger there is an urgent, call assistance button which also provides the police with their current location.

Linked to a student's safety is drinking responsibly. It's already possible to monitor alcohol drinking using apps like WiseDrinking which monitors an individual's drinking, based on their size, gender, time of last meal etc. provides advice on unsafe drinking and helps with calling a taxi or using public transport. In future, on the intelligent campus a student could allow their drinking in campus bars to be monitored to allow student welfare services to be alerted when drinking is excessive.

Keeping healthy

Similarly the intelligent campus could provide students with help in eating healthily on a budget, providing healthy recipes and shopping advice. The Internet of Things could monitor fridge contents and food which is out of date.

There are a large number of mental health apps now available, many of which could help with student wellbeing. The University of Edinburgh student counselling service provides a useful review of a number of these apps. They look at issues such as panic attacks, wellbeing, mental health, anxiety and mood improvement. Coventry University's CU Health and Wellbeing app " provides information to Coventry University Students on student support services, interactive maps to NHS providers and campus buildings, and emergency contacts and help. " While this app is not really taking advantage of intelligent campus developments it does demonstrate that universities and colleges are keen to improve the health and wellbeing of their students.

In future the intelligent campus could move beyond offering advice to monitoring student behaviour and intervening if necessary. If their typical behaviour in terms of alcohol consumption, coffee drinking, movement patterns, absence from lectures, missing meetings, etc. changes, alerts and interventions could be planned by tutors or student welfare services.

Keeping Fit

Many students and staff are now using wearable fitness trackers. These could be integrated with the intelligent campus, for example, providing walking routes around the campus to achieve activity targets. Campus communities of activity tracker users could be supported creating social opportunities and even a level of competition. The university or colleges sports facilities, programmes and courses could also be integrated with the individual's fitness targets and goals providing further support. If confidentiality issues are not considered a problem, wearable fitness trackers could provide the intelligent campus with huge quantities of data about students. This could include their sleep patterns, or lack of sleep, since many activity trackers have basic sleep tracking facilities, others can provide heart rate data which could be used to provide information about individuals with high stress levels or other health issues.

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As well as enabling a student to achieve their academic goals, colleges and universities have a responsibility for a student's wellbeing. Their health, both physical and mental, security and safety, are all important to institutions. The development of intelligent campuses can offer considerable support for student wellbeing.

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Author: James Clay

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Use Case: Emotional Recognition in the Library

Could we use emotional recognition technology to discover when students are "troubled" or "in need of help" and then make appropriate interventions to support them in their studies? With constant emotional recognition could we start to understand what a "troubled" user's expression is.

What's the issue?

Users of the library often need help and support when attempting to find resources, use resources or other aspects of their learning. Users may not know where to find resources, or even what resources may be useful for them at that point in time. They may be using resources, but unsure if they are appropriate or relevant. Students may be using the library as a study space and are having other trouble with aspects of their studies.

What are the current solutions?

Most solutions rely on the students seeking support or help from dedicated staff within the library, The implication is they know they can seek support and they can find a relevant member of staff to ask. There is also the assumption that the student knows they need help, they may not see that as a possible option.

Another solution is for library staff to "discover" which students are having issues and trying to guess which students need support.

Emotional recognition software could constantly scan users and using appropriate algorithms flag students that may need support and direct staff to them as quickly as possible.

Combined with facial recognition, details about the student could be provided to the staff to enable them to tailor and personalise the support the student needs.

What about ethical and other issues?

You can imagine the strong feelings that such a system could evoke, with the idea that users of the library are being constantly scanned and monitored. Consent to be part of a system would be critical, though often consent is not sought for non-technological solutions. Another challenge would be the concern that such scanning and data gathering may be used for other purposes outside support in the library.

Are there any current examples?

Emotion recognition and emotion analysis are being studied by companies and universities around the world, most are looking at the technology to improve marketing and effectiveness of video content.

So how could it work?

Humans can already recognise emotions in variable abilities, any kind of system to scan and recognise emotion using technology could inform and advise staff faster and quicker. Enabling a rapid response to potential difficulties.

Software already exists that utilises artificial intelligence to predict "attitudes and actions based on facial expressions".

The challenge will be ensuring staff have the appropriate interventions to hand having identified those students who may be have problems.

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Could we use emotional recognition technology to discover when students are "troubled" or "in need of help" and then make appropriate interventions to support them in their studies? With constant emotional recognition could we start to understand what a "troubled" user's expression is.

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Collaboration among libraries, Archives and Museums

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Collaboration among libraries, Archives and Museums

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Authors: Diane M. Zorich; Gunter Waibel; and Ricky Erway.

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Gartner Top 10 Strategic Technology Trends for 2018

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Gartner Top 10 Strategic Technology Trends for 2018

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Contributor: Casey Panetta

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Explore the latest: Your Detailed Guide to Gartner Top 10 Strategic Technology Trends 2023

How do designers make cars safer? They treat them like a school of fish. Safe Swarm, recently unveiled by Honda, uses vehicle-to-vehicle communication to allow cars to pass information on to other cars in the vicinity. For example, alerts about an accident miles up the road could be relayed to cars miles back, enabling them to operate collaboratively and intelligently to avoid accidents and mitigate traffic.

The evolution of intelligent things, such as collective thinking car swarms, is one of 10 strategic trends with broad industry impact and significant potential for disruption.

"The continuing digital business evolution exploits new digital models to align more closely the physical and digital worlds for employees, partners and customers," says David Cearley, vice president and Gartner Fellow, at Gartner 2017 Symposium/ITxpo in Orlando, Florida. "Technology will be embedded in everything in the digital business of the future."

The intelligent digital mesh

Gartner calls the entwining of people, devices, content and services the intelligent digital mesh. It's enabled by digital models, business platforms and a rich, intelligent set of services to support digital business.

Intelligent: How AI is seeping into virtually every technology and with a defined, well-scoped focus can allow more dynamic, flexible and potentially autonomous systems.

Digital: Blending the virtual and real worlds to create an immersive digitally enhanced and connected environment.

Mesh: The connections between an expanding set of people, business, devices, content and services to deliver digital outcomes.

Intelligent

Trend No. 1: AI foundation

The ability to use AI to enhance decision making, reinvent business models and ecosystems, and remake the customer experience will drive the payoff for digital initiatives through 2025.

Given the steady increase in inquiry calls, it's clear that interest is growing. A recent Gartner survey showed that 59% of organizations are still gathering information to build their AI strategies, while the remainder have already made progress in piloting or adopting AI solutions.

Although using AI correctly will result in a big digital business payoff, the promise (and pitfalls) of general AI where systems magically perform any intellectual task that a human can do and dynamically learn much as humans do is speculative at best. Narrow AI, consisting of highly scoped machine-learning solutions that target a specific task (such as understanding language or driving a vehicle in a controlled environment) with algorithms chosen that are optimized for that task, is where the action is today. "Enterprises should focus on business results enabled by applications that exploit narrow AI technologies and leave general AI to the researchers and science fiction writers," says Cearley.

Trend No. 2: Intelligent apps and analytics

Over the next few years every app, application and service will incorporate AI at some level. AI will run unobtrusively in the background of many familiar application categories while giving rise to entirely new ones. AI has become the next major battleground in a wide range of software and service markets, including aspects of ERP. "Challenge your packaged software and service providers to outline how they'll be using AI to add business value in new versions in the form of advanced analytics, intelligent processes and advanced user experiences," notes Cearley.

Intelligent apps also create a new intelligent intermediary layer between people and systems and have the potential to transform the nature of work and the structure of the workplace, as seen in virtual customer assistants and enterprise advisors and assistants.

"Explore intelligent apps as a way of augmenting human activity, and not simply as a way of replacing people," says Cearley. Augmented analytics is a particularly strategic growing area that uses machine learning for automating data preparation, insight discovery and insight sharing for a broad range of business users, operational workers and citizen data scientists.

Trend No. 3: Intelligent things

Intelligent things use AI and machine learning to interact in a more intelligent way with people and surroundings. Some intelligent things wouldn't exist without AI, but others are existing things (i.e., a camera) that AI makes intelligent (i.e., a smart camera.) These things operate semiautonomously or autonomously in an unsupervised environment for a set amount of time to complete a particular task. Examples include a self-directing vacuum or autonomous farming vehicle. As the technology develops, AI and machine learning will increasingly appear in a variety of objects ranging from smart healthcare equipment to autonomous harvesting robots for farms.

As intelligent things proliferate, expect a shift from stand-alone intelligent things to a swarm of collaborative intelligent things. In this model, multiple devices will work together, either independently or with human input. The leading edge of this area is being used by the military, which is studying the use of drone swarms to attack or defend military targets. It's evident in the consumer world in the opening example showcased at CES, the consumer electronics event.

Digital

Trend No. 4: Digital twins

A digital twin is a digital representation of a real-world entity or system. In the context of IoT, digital twins are linked to real-world objects and offer information on the state of the counterparts, respond to changes, improve operations and add value. With an estimated 21 billion connected sensors and endpoints by 2020, digital twins will exist for billions of things in the near future. Potentially billions of dollars of savings in maintenance repair and operation (MRO) and optimized IoT asset performance are on the table, says Cearley.

In the short term, digital twins offer help with asset management, but will eventually offer value in operational efficiency and insights into how products are used and how they can be improved.

Outside of the IoT, there is a growing potential to link digital twins to entities that are not simply "things." "Over time, digital representations of virtually every aspect of our world will be connected dynamically with their real-world counterparts and with one another and infused with AI-based capabilities to enable advanced simulation, operation and analysis," says Cearley. "City planners, digital marketers, healthcare professionals and industrial planners will all benefit from this long-term shift to the integrated digital twin world." For example, future models of humans could offer biometric and medical data, and digital twins for entire cities will allow for advanced simulations.

Trend No. 5: Cloud to the edge

Edge computing describes a computing topology in which information processing and content collection and delivery are placed closer to the sources of this information. Connectivity and latency challenges, bandwidth constraints and greater functionality embedded at the edge favors distributed models. Enterprises should begin using edge design patterns in their infrastructure architectures - particularly for those with significant IoT elements. A good starting point could be using colocation and edge-specific networking capabilities.

While it's common to assume that cloud and edge computing are competing approaches, it's a fundamental misunderstanding of the concepts. Edge computing speaks to a computing topology that places content, computing and processing closer to the user/things or "edge" of the networking. Cloud is a system where technology services are delivered using internet technologies, but it does not dictate centralized or decentralized service delivering services. When implemented together, cloud is used to create the service-oriented model and edge computing offers a delivery style that allows for executions of disconnected aspects of cloud service.

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cio technology and innovation leadership

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Gartner Top 10 Strategic Technology Trends For 2018

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Explore the latest: Your Detailed Guide to Gartner Top 10 Strategic Technology Trends 2023 How do designers make cars safer? They treat them like a school of fish. Safe Swarm, recently unveiled by Honda, uses vehicle-to-vehicle communication to allow cars to pass information on to other cars in the vicinity.