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Accelerating the future of privacy through SmartData agents

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Imagine a future where you can communicate with your smartphone – or whatever digital extension of you exists at that time – through an evolved smart digital agent that readily understands you, your needs, and exists on your behalf to procure the things and experiences you want. What if it could do all this while protecting and securing your personal information, putting you firmly in control of your data?

Dr. George Tomko, University of Toronto

Dr. George Tomko, University of Toronto

Dr. George Tomko Ph.D, Expert-in-Residence at IPSI (Privacy, Security and Identity Institute) at the University of Toronto, Adjunct Professor in Computer Science at Ryerson University, and Neuroscientist, believes the time is ripe to address the privacy and ethical challenges we face today, and to put into place a system that will work for individuals, while delivering effective business performance and minimizing harms to society at large. I had the privilege of meeting George to discuss his brainchild, SmartData: the development of intelligent agents and the solution to data protection.

As AI explodes, we are witnessing incident after incident from technology mishaps to data breaches, to data misuse, and erroneous and even deadline outcomes. My recent post, Artificial Intelligence needs to Reset advances the need to take a step back, slow down the course of AI, and examine these events with a view to educate, fix, prevent and regulate towards effective and sustainable implementations.

Dr. Tomko is not new to the topic of privacy. He also invented Biometric Encryption as well as the Anonymous Database in the early 90’s.  His invention of SmartData was published SmartData: Privacy Meets Evolutionary Robotics, co-authored with Dr. Ann Cavoukian, former 3-term Privacy Commissioner in Ontario and inventor of Privacy by Design. This led to his current work, Smart Data Intelligent Agents, the subject of this article.

There is an inherent danger with the current model today. How the internet evolved was not its intended path. Tim Berners-Lee envisioned an open internet, owned by no one,

…an open platform that allows anyone to share information, access opportunities and collaborate across geographical boundaries…

This has been challenged by the spread of misinformation and propaganda online has exploded partly because of the way the advertising systems of large digital platforms such as Google or Facebook have been designed to hold people’s attention…

People are being distorted by very finely trained AIs that figure out how to distract them.

What has evolved is a system that’s failing. Tomko points to major corporations and digital gatekeepers who are accumulating the bulk of the world’s personal data:

He who has the personal data has the power, and as you accumulate more personal information (personally identifiable information, location, purchases, web surfing, social media), in effect you make it more difficult for competitors to get into the game. The current oligopoly of Facebook, Google, Amazon etc will make it more difficult for companies like Duck Duck Go and Akasha to thrive.

That would be okay if these companies were to utilize the data in accordance with the positive consent of the data subject for the primary purpose intended and protected it against data hacking. However, we know that’s not happening. Instead, they are using it for purposes not intended, selling the data to third parties, transferring it to government for surveillance, often without a warrant for probable cause.

Tomko asserts if Elon Musk and the late Stephen Hawking are correct about the potential of a dystopian-like AI popularized by Skynet in The Terminator series, this is likely if the AI has access to large amounts of personal data centralized into databases. While this implies an AI with malevolent intentions, humans are relentlessly innovative and Tomko argues for the importance of putting roadblocks in place before this happens.

Enter SmartData. This is the evolution of Privacy by Design, which shifts control from the organization and places it directly in the hands of the individual (the data subject).

SmartData empowers personal data by, in effect, wrapping it in a cloak of intelligence such that it now becomes the individual’s virtual proxy in cyberspace. No longer will personal data be shared or stored in the cloud as merely data, encrypted or otherwise; it will now be stored and shared as a constituent of the binary string specifying the neural weights of the entire SmartData agent. This agent proactively builds-in privacy, security and user preferences, right from the outset, not as an afterthought.

For SmartData to succeed, it requires a radical, new approach – with an effective separation from the centralized models which exist today.

Privacy Requires Decentralization and Distribution

Our current systems and policies present hurdles we need to overcome as privacy becomes the norm. The advent of Europe’s GDPR is already making waves and challenging business today. Through GDPR’s Article 20 (The Right to Data Portability) and Article 17 (The Right to Be Forgotten), the mechanisms to download personal data, plus the absolute deletion of data belie current directives and processes. Most systems ensure data redundancy, therefore data will always exist. Systems will need to evolve to fully comply with these GDPR mandates. In addition, customer transactions on private sites are collected, analyzed, shared and sometimes sold with a prevailing mindset that data ownership is at the organizational level.

Tomko explains the SmartData solution must be developed in an open source environment.

A company that says: “Trust me that the smart agent or app we developed has no “back-door” to leak or surreptitiously share your information,” just won’t cut it any longer. Open source enables hackers to verify this information. I believe that such a platform technology will result in an ecosystem that will grow, as long as there is a demand for privacy.

Within this environment, a data utility within the SmartData platform can request all personal data under GDPR-like regulations from the organizational database. As per the SmartData Security Structure, each subject’s personal data is then cleaned and collated into content categories e.g. A = MRI data, B = subscriber data. They will be de-identified, segmented, encrypted and placed in these locked boxes (files in the cloud) identified by categorized metatags. A “Trusted Enclave” like Intel’s SGX will be associated with each data subject’s personal data. The enclave will generate a public/private key pair and output the public key to encrypt the personal data by category.

Today, information is stored and accessed by location. If breaches occur, this practice increases the risk of exposure as information about data subjects are bundled together. By categorizing and storing personal information by content, this effectively prevents personal identity to be connected with the data itself. Only SmartData will know its data subjects and pointers to their unique personal information, accessed by a unique private key.

SmartData Security Structure, George Tomko

SmartData Security Structure, George Tomko

Ensuring Effective Performance while Maintaining Individual Privacy

Organizations who want to effectively utilize data to improve efficiencies and organizational performance will take a different route to achieve this. How do companies analyze and target effectively without exposing personal data? Tomko declares that using Federated Learning, to distribute data analytics such as Machine Learning(ML) is key:

Federated Learning provides an alternative to centralizing a set of data to train a machine learning algorithm, by leaving the training data at their source. For example, a machine learning algorithm can be downloaded to the myriad of smartphones, leveraging the smartphone data as training subsets. The different devices can now contribute to the knowledge and send back the trained parameters to the organization to aggregate.  We can also substitute smartphones with the secure enclaves that protect each data subject’s personal information.

Here’s how it would work: An organization wants to develop a particular application based on machine learning, which requires some category of personal data from a large number of data-subjects as a training set. Once it has received consent from the data subjects, it would download the learning algorithm to each subject’s trusted enclave. The relevant category of encrypted personal data would then be inputted, decrypted by the enclave’s secret key, and used as input to the machine learning algorithm. The trained learning weights from all data-subjects’ enclaves would then be sent to a master enclave within this network to aggregate the weights. This iteration would continue until the accuracies are optimized. Once the algorithm is optimized, the weights would then be sent to the organization. Tomko affirms,

 

The organization will only have the aggregated weights that had been optimized based on the personal data of many data subjects. They would not be able to reverse engineer and determine the personal data of any single data subject. The organization would never have access to anyone’s personal data, plaintext or otherwise, however, would be able to accomplish their data analytic objectives.

Federated Learning - Master Enclave, George Tomko

Federated Learning – Master Enclave, George Tomko

Building a Secure Personal Footprint in the Cloud

To ensure personal web transactions are secure, a person will instruct his SmartData agent to, for example, book a flight. The instruction is transmitted to the cloud using a secure protocol such as IPSec. This digital specification (a binary string) is decrypted and downloaded to one of many reconfigurable computers, which will interpret the instructions.

Natural language (NLP) would convert the verbal instructions into formal language, as well as the encoded communications, back and forth between subject and organization to facilitate the transaction, eliciting permission for passport and payment information. What’s different is the development of an agreement (stored on the Blockchain) that confirms consented terms of use between the parties. It also adds an incentive component through cryptocurrency that enables the data subject to be compensated for their information, if required. This mechanism would be used before every transaction to ensure transparency and expediency between parties.

Tomko realizes Blockchain has its limitations:

Everyone wants to remove the intermediary and the crypto environment is moving quickly. However, we can’t rely on Blockchain alone for privacy because it is transparent, and we can’t use it for computation because it is not scalable.

AI as it exists today is going through some stumbling blocks. Most experiments are largely within ANI: Artificial Narrow Intelligence, with models and solutions built for very specific domains, which cannot be transferred to adjacent domains. Deep Learning has its limitations. The goal of SmartData is to develop a smart digital personal assistant to serve as a proxy for the data-subject across varied transactions and contexts. Tomko illustrates,

With current Deep Learning techniques, different requests such as ‘Hey SmartData, buy me a copy of …” or “book me a flight to…” encompass different domains, and accordingly, require large sets of training data specific to that domain. The different domain-specific algorithms would then need to be strung together into an integrated whole, which, in effect, would become SmartData. This method would be lengthy, computationally costly and ultimately not very effective.

The promise of AI: to explain and understand the world around us and it has yet to reveal itself.

Tomko explains:

To date, standard Machine Learning (ML) cannot achieve incremental learning that is necessary for intelligent machines and lacks the ability to store learned concepts or skills in long-term memory and use them to compose and learn more sophisticated concepts or behaviors. To emulate the human brain to explain and generally model the world, it cannot be solely engineered. It has to be evolved within a framework of Thermodynamics, Dynamical Systems Theory and Embodied Cognition.

Embodied Cognition is a field of research that “emphasizes the formative role that both the agents’ body and the environment will play in the development of cognitive processes.” Put simply, these processes will be developed when these tightly coupled systems emerge from the real-time, goal-directed interactions between the agents and their environments, and in SmartData’s case, a virtual environment. Tomko notes the underlying foundation of intelligence (including language) is action.

Actions cannot be learned in the traditional ML way but must be evolved through embodied agents. The outcomes of these actions will determine whether the agent can satisfy the data subject’s needs.

Tomko references W. Ross Ashby, a cybernetics guru from the 50’s, who proposed that every agent has a set of essential variables which serve as its benchmark needs, and by which all of its perceptions and actions are measured against. The existential goal is to always satisfy its needs. By using this model (see below), we can train the agent to satisfy the data subject’s needs, and retain the subject’s code of ethics. Essential variables are identified that determine the threshold for low surprise or high surprise. Ideally, the agent should try to maintain a low-surprise and homeostatic state (within the manifold) to be satisfied. Anything outside the manifold, i.e., high surprise should be avoided. Tomko uses Ashby’s example of a mouse, who wants to survive. If a cat is introduced, a causal model of needs is built such that the mouse uses its sensory inputs compared to its benchmark needs to determine how it will act when a cat is present and maintain its life-giving states.

Apply this to individual privacy. As per Tomko,

The survival range will include parameters for privacy protection. Therefore, if the needs change or there is a modified environment or changing context the agent will modify its behavior automatically and adapt because its needs are the puppet-master.

This can be defined as a reward function. We reward actions that result in low surprise or low entropy. For data privacy, ideally, we want to avoid any potential actions that would lead to privacy violations equating to high surprise (and greater disorder).

 

Manifold of Needs, George Tomko

Manifold of Needs, George Tomko

Toronto’s Sidewalk Labs: The Need for Alternative Data Practices

At the time of writing this article, Dr. Ann Cavoukian, Expert-in-Residence at Ryerson University, former 3-term Privacy Commissioner, resigned as an advisor to Sidewalk Labs, in Toronto, a significant project powered by Alphabet, which aimed to develop one of the first smart cities of privacy in the world. Cavoukian’s resignation resulted in a media coup nationally because of her strong advocacy for individual privacy. She explains,

My reason for resigning from Sidewalk Labs is only the tip of the iceberg of a much greater issue in our digitally oriented society.  The escalation of personally identified information being housed in central databases, controlled by a few dominant players, with the potential of being hacked and used for unintended secondary uses, is a persistent threat to our continued functioning as a free and open society.

Organizations in possession of the most personal information about users tend to be the most powerful. Google, Facebook and Amazon are but a few examples in the private sector… As a result, our privacy is being infringed upon, our freedom of expression diminished, and our collective knowledge base outsourced to a few organizations who are, in effect,  involved in surveillance fascism. In this context, these organizations may be viewed as bad actors; accordingly, we must provide individuals with a viable alternative…

The alternative to centralization of personal data storage and computation is decentralization – place all personal data in the hands of the data-subject to whom it relates, ensure that it is encrypted, and create a system where computations may be performed on the encrypted data, in a distributed manner… This is the direction that we must take, and there are now examples of small startups using the blockchain as a backbone infrastructure, taking that direction.  SmartData, Enigma, Oasis Labs, and Tim Berners-Lee’s Solid platform are all developing methods to, among other things, store personal information in a decentralized manner.

Other supporters of Dr. George Tomko concur:

Dr. Don Borrett, a practicing neurologist with a background in evolutionary robotics, with a Masters from the Institute for the History and Philosophy of Science and Technology for the University of Toronto states:

By putting control of personal data back into the hands of the individual, the SmartData initiative provides a framework by which respect for the individual and responsibility for the collective good can be both accommodated.

Bruce Pardy is a Law Professor at Queen’s University, who has written on a wide range of legal topics: human rights, climate change policy, free markets, and economic liberty, among others and he declares:

The SmartData concept is not just another appeal for companies to do better to protect personal information. Instead, it proposes to transform the privacy landscape. SmartData technology promises to give individuals the bargaining power to set their own terms for the use of their data and thereby to unleash genuine market forces that compel data-collecting companies to compete to meet customer expectations.

Dr. Tomko is correct! The time is indeed ripe, and SideWalk Labs, an important experiment that will vault us into the future, is an example of the journey many companies must take to propel us into an inevitability where privacy is commonplace.

This originally appeared om Forbes.

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Growing world-class scaleup hubs through global lessons

Dean Hopkins

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Dean Hopkins, CEO at OneEleven, discusses how global scaleup hubs can learn from each other in order to build outstanding scaleups.

Any time a new global city or region emerges as a technology or innovation hub, the inevitable comparisons to Silicon Valley begin. New York as Silicon Alley, Israel as Silicon Wadi, and Toronto was recently dubbed Maple Valley to much scorn.

But it’s time for globally emergent innovation hubs to look beyond Silicon Valley as they work to build scaleup success, with each location learning from the specific lessons of one another to help all players in the community succeed.

Outside the original Valley, collaboration, diversity and connections into other ecosystems are major strategic advantages for any hub that wants to scale faster – more connections, more funding, more talent, more resources and more stories to share to teach others.

Just look at Stockholm: With a population of only one million, it has developed more Unicorns per capita than any other innovation ecosystem outside Silicon Valley. Among other things, connecting into other major hubs helped propel growth and seed opportunity.

With OneEleven now established in the UK, we’re applying lessons from two leading hubs — London and Toronto — to guide our strategy and propel our value. Both cities embody hard-earned scaleup lessons, like specialization, building ecosystem partnerships and leveraging the power of diverse leadership, that we believe are key to ecosystem and company success.

Focus on growing the greatest verticals

London has built an ecosystem around its strengths.

The city is by far the leading source of fintech innovation worldwide: it has the greatest concentration of fintechs and the largest workforce in fintechit dwarfs everywhere else even New York. In the first quarter of 2017, London saw $421 million invested in its fintech industry pushing New York out of the top spot for fintech investment. The City of London has worked with a variety of institutions to rally behind this emphasis on fintech, bringing together government, educational institutions and various sources of funding to embrace the fintech ethos.

The lesson to be learned from London’s focus on fintech is that innovation hubs need to concentrate their efforts in certain sectors where they already stand out as a global leader.

In Toronto, we’re starting to see a lot going on in the deep AI tech space, through the Vector Institute and other organizations building on a research base of over 30 years by Dr. Geoffrey Hinton and his colleagues. Of course, there’s room for improvement. While research labs are popping up regularly, with big partners involved, Toronto and Canada are lagging when it comes to patents and application of AI tech. As we build up this sector of our innovation ecosystem, we have to develop a well-rounded AI industry that includes a robust IP regime to keep AI innovation in Canada.

Diversity in leadership

Both London and Toronto also boast the highest demographic diversity of global cities, and demonstrate how valuable entrepreneurial leadership from all over the world can be. Forty percent of London residents classifying themselves as other than white according to a 2011 census, and that diversity powers the tech and innovation ecosystem in the city. Recent research shows that immigrants and people from minority backgrounds in the UK are twice as likely to be early-stage entrepreneurs.

Toronto is similarly diverse in its population, and talent is one of the reasons the city is seeing global recognition as an innovation hub.

Canada’s fast-track visa program prioritizes highly skilled workers and entrepreneurs  and was created as a talent magnet for Toronto especially – last year MaRS released survey results showing 45 percent of Toronto tech companies made international hires in 2017 alone, and 35 percent of respondents used the visas to hire.

Other scaleup hubs could build valuable leadership and collaboration from a similar approach to entrepreneurship: one which looks to bring in more diverse, global talent on the leadership side, as well as the wider talent side. Scaleup communities have to be competitive on the world stage by inspiring people from all over the world to come and build their businesses there, as a lack of immigration and global perspective can starve an ecosystem of oxygen.

Culture of collaboration

We’re very fortunate in Toronto to have a culture of collaboration that starts at the earliest stages of entrepreneurship, and continues throughout company growth. There’s a strong expectation that you will work together, and for that reason, forming a community in Toronto is almost a matter of course.

Hubs like MaRS, 111 and the DMZ, for example, have opened up prime real estate to provide space for young companies to grow and to foster their developing businesses. Canadians have proven they are wired differently and Toronto’s collaborative and inclusive culture is one of its strongest competitive advantages.

In London, there’s a hyper-competitive environment for businesses, and perhaps not as naturally collaborative of an environment. That might just be because the city has only just recently seen an effort made to boost that kind collaboration from organizations like the Scaleup Institute and Tech London Advocates.

But collaboration between government, academia and business is one of the things that makes London a world-class scaleup hub.

Collaboration between groups tends to be verticalized in the UK, with TheCityUK being a prime example; the industry-led body that represents UK-based financial and professional services companies showed that collaboration between financial institutions and fintech companies can speed up the process of creating innovative products and services. By looking at IP, regulatory compliance, data protection and privacy, TheCityUK provided seven possible models for collaboration between banks and fintech companies.

Big scaleup success stories can also influence the effort to increase collaboration in scaleup hubs — and London has some amazing stories to tell.

Renewable energy company Bulb grew from 85,000 customers to 870,000 in the space of 12 months, becoming one of the fastest-growing scaleups in the UK. The company’s founders Hayden Wood and Amit Gudka are immensely proud of their place in London’s ecosystem. This is how how big names in a scaleup hub can advocate for an entire community.

For our part at OneEleven, we’ll work hard to build up that kind of collaborative community and collective effort as we continue to expand into London’s innovation ecosystem. We want to ensure that the success of these companies continues past their early stage, into growth and on into the billion-dollar club. The middle chapter is currently not being written in London — despite early stage support for companies and big success stories — and that’s what 111 is here to address.

Global scale through collaboration

Innovation hubs around the world can also work together to take the friction out of companies expanding between markets. Furthermore, cooperating markets can increase their competitiveness by promoting an exchange of innovative business practices, and reap the economic benefits that scaleups can bring to innovation ecosystems.

London and Toronto are a good example of global collaboration, as they the two cities have begun to explore greater cooperation when it comes to facilitating expansion between hubs.

The Mayor of London’s promotional agency London & Partners has opened an office in Toronto to better encourage Canadian businesses seeking to expand to consider London for their next destination, and to support UK businesses seeking expansion into Canada’s market. Over the last decade, the organization says 44 London businesses have expanded into Toronto and 118 Canadian businesses have set up shop in London during that same period.

This is only the beginning when it comes to proper cooperation between these two cities: government, academia and innovation hubs should work together to encourage scaleups in their efforts to expand between international markets.

Greater than the global sum of our parts

At OneEleven, it seems to us that the unique evolution, and now collaboration, between the London and Toronto ecosystems signals the rise of a global network of innovation that is in its early stages.  Such a global network, powered by the diversity of each market, promises to have a dramatic effect on the ability for scaling companies to access talent, customers, investors and partners much more easily.  We are excited to be a part of the rise of this globally connected and collaborative ecosystem that builds on what was started in Silicon Valley, but brings innovation into the more global and highly connected digital present.

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Robots aren’t taking our jobs — just yet

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A robot may take your job one day, but so far there is little sign of this happening, according to analysis from the World Bank. While there are predictions about machines taking more jobs associated with humans, the pace is relatively slow.

The rise of automation has been reviewed by the World Bank chief economist, based on data collated from a number of industries. The news is mixed. Most advanced economies seen a decline in industrial jobs since the year 2000 and a rise of robots, in other parts of the world, notably East Asia, there has been a net gain of manufacturing jobs and little sign of robots replacing these types of roles.

Overall, estimations of the impact of automation have been less optimistic. For example, in 2017 Oxford University researchers Dr. Michael Osborne and Dr. Carl Frey interpreted data which suggested that over fifty percent of jobs in a developed economy are vulnerable in terms of humans being replaced by machines. Similarly, the World Economic Forum forecasts that machines and automated software will be handling fully half of all workplace tasks by 2025.

In contrast, some other predictions are overtly positive, such as a report from Siemens, that suggests the ‘fourth industrial revolution’ will add billions to economies and that, instead of fearing robots, the drive towards automation will actually generate more jobs.

In relation to the World Bank analysis, World Bank’s Chief Economist Pinelopi Koujianou Goldberg, interviewed by Bloomberg, states: “This fear that robots have eliminated jobs — this fear is not supported by the evidence so far.”

To support this she draws upon the analysis contained within the World Development Report 2019, subtitled “The Changing Nature of Work.” The report is considerably pro-technology, indicating: “Work is constantly reshaped by technological progress. Firms adopt new ways of production, markets expand, and societies evolve. Overall, technology brings opportunity, paving the way to create new jobs, increase productivity, and deliver effective public services.”

However, as Goldberg discusses in the World Bank report, the range of different work undertaken will alter, with employees far more likely to hold several different jobs over the course of their careers instead of holding down a position with the same employer for decades.

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Proptech set to disrupt real estate in 2019

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The real estate industry is not only the oldest but the biggest of our business entities, and today, technology is starting to disrupt it.

There’s a word for this kind of real estate technology. It’s called “proptech,” a technology developed specifically for the property market. Proptech brings together multiple facets of the industry — from planning and construction, to the sale of a property.

Proptech platforms allow realtors to remotely present on property development and sale information, but this is just one small part of what can be done with this technology. The platform also includes online services that transfer digitized documents to the cloud (which can then be digitally signed) and allow access to regulations pertaining to a particular property.

How proptech works

Devin Tu is the founder and CEO of MapYourProperty in Toronto, Canada. Tu’s company makes use of a digital tool that gives real estate developers a digital interface to access layers of important information about a property, including zoning bylaws and nearby proposals.

To show how the proptech app works, Tu described how it served one client. “We had a client looking at a site in North York that they thought was ideal. But then, they used our tool, which scanned 25 different regulations and checked developments in the area in real time,” said Tu. “It turns out they had missed a key floodplain regulation.”

Tu went on to say the client almost got stuck with a $10 million piece of property he would not have been able to develop. The area remains a parking lot today.

Regarding the developing trend of proptech, Tu notes that the recent growth of the property industry has come about because of necessity. There’s a shortage of land and competition is increasing, forcing realtors and clients to make quick decisions.

Property industry plays catch-up

Frank Magliocco, a partner at PwC Canada who specializes in the housing market, told Mortgage Broker News that the real estate industry has been historically slow to embrace new technology.

“I think what you’re going to see now is a fairly significant ramp up in embracing that technology once it becomes more mainstream,” said Magliocco. “It’ll be increasingly important to remain and be competitive in the marketplace. Once you see these technologies prove out, you’ll see more and more adoption.”

It looks like Canada is going to end up as one of the next major regions for property technology innovation. Besides MapYourProperty, several large Canadian organizations have made announcements of their move into the PropTech space, including Toronto-based Colliers International and Brookfield.

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