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A Collection of Articles written by Andy Pardoe
The Golden Age of Algorithms #GoldenAgeAI

The Golden Age of Algorithms #GoldenAgeAI

It seems obvious that the future of wealth and prosperity will be driven by expertise with advanced algorithms. Some call it the next Space Race, others the next Arms Race, either way, we are entering the golden age of the algorithm. This is not only a national race, but also a commercial and individual race too. And calling this an Arms Race or Digital Warfare is probably more accurate than a Space Race.

We have already heard about an AI fighter pilot that is more performant and capable than the best of the best human test pilots, and AI applications are helping to defend against security cyber attacks. Computers are hacking computers. How is this not digital warfare?

But this isn’t just about traditional warfare, this is about business and commercial success too. Only those companies that embrace the benefits that advance algorithms can deliver will survive. This will be the new major differentiator for firms. One only has to look at some of the trailblazing firms to already see this happening. Business models are changing, dramatically, with global startups, unicorns, having defined the landscape of many traditional businesses.

Algorithms will have a massive impact on individuals too. In many cases they already are, from mortgage approvals, medical diagnosis, recommendations for purchases, to who you should date, they touch our lives in so many different and usually hidden ways that one can argue we are already being shaped and controlled by the power of the algorithm. We do this as we trust that data holds the universal truth and that we are making better decisions by putting our faith in the algorithms that analyse these huge datasets.

This raises alot of questions around AI Ethics, AI Governance and AI Safety, these are hot topics that many high profile advocate is highlighting a need for more investment and research on these subjects to support the rise of AI in general. Without these support frameworks the algorithms could spin out of control.

Are you ready for the golden age of algorithms?

#GoldenAgeAI

All Party Parliamentary Group for AI – Written Evidence Submission

Dr Andrew Pardoe – Written evidence (AIC0020)

What are the implications of artificial intelligence?

Introduction

Dr Andy Pardoe has a doctorate in artificial intelligence and is the founder of Informed.AI, a community of websites for AI supporting those interested to learn more or working in the industry. Listed by IBM Watson in the Top 20 of Global AI Influencers, listed just below Elon Musk. He runs the annual global achievement awards for Artificial Intelligence. A member of the British Computer Society Special Group for Artificial Intelligence committee. Currently work for Credit Suisse designing their own Machine Learning Platforms and Applications. Andy is an International Speaker on Artificial Intelligence, Machine Learning and Robotic Automation.

These are my own personal views.

Questions to be address

  1. The current state of artificial intelligence.
  2. The pace of technological change and the development of artificial intelligence
  3. The impact of artificial intelligence on society
  4. The public perception of artificial intelligence
  5. The sectors most, and least likely, to benefit from artificial intelligence
  6. The data-based monopolies of some large corporations
  7. The ethical implications of artificial intelligence
  8. The role of the Government and
  9. The work of other countries or international organisations.

The current state of artificial intelligence.

While the field of AI has been around since the 1950s, it is only in the last 5 to 10 years that a number of factors have come together to establish an unstoppable progression towards super intelligence and the singularity. Those factors being, (1) the advent of large qualities of data facilitated by big data technologies like Hadoop and Big Table, (2) via Cloud platforms access to scalable computing infrastructure, (3) via the gaming industry multi-processor technologies in the form of graphical processing units (GPUs) and lastly (4) advanced learning algorithms and topologies that enable deep learning neural networks and reinforcement learning. Currently most applications of AI are what is call Narrow or Specific AI (meaning a very targeted application) which a number of companies and research institutions are working on AGI (artificial general intelligence) which holds the promise of delivering super intelligence. However, most knowledgeable commentators believe that AGI and Super Intelligence will not happen for another 20-40 years.

The pace of technological change and the development of artificial intelligence

As detailed by the World Economic Forum, we are at the start of the fourth industrial revolution, and what is most evident is that the rate of change for technology development is increasing at an exponential rate, and we are at the inflection point, where we will see in the next 10-15 years the same amount of change as we have seen in the last hundred years. It is clear to see that the next 25-50 years will be an amazing time of change and transformation. The level of complexity and intelligence shown by our machines will be astonishing and that of science fiction for many. With so many different technologies (Robotics, 3D printing, crypto-currencies and block chain, Virtual and Augmented Reality) becoming mainstream and being integrated together the possibilities are endless for the enabling technology of AI.

The impact of artificial intelligence on society

As with any industrial revolution the impact on society is immense, and will be perceived as both positive and negative, The challenge with the fourth industrial revolution is that its potential to impact every single profession and industry, large and small. Even those industries that currently appear to have a low dependency on technology are also in scope for impact. Both low and high skilled professions will suffer from job displacement, and the need for a universal basic income is real. Highly skilled professionals will learn their trade from AI agents in the future, as the normal method to learn wont be available as AI agents will be performing the basic functions that were previously used to learn the trade. Every role will be augmented to some degree by AI agents. In the near future entire companies could be fully automated, with only a handful of human staff who own the company, and deal with operational changes needed, with all other tasks being managed by computer and robotics. Another aspect impacting society will be the social interactions of intelligent robots and androids with humans, and more specifically how humans will consider and react to andriods.

The public perception of artificial intelligence

As detailed by the Royal Society Report on AI that surveyed the public, many do not know the specific details of AI. but most understand the applications of AI, including self-driving cars and personal assistants on mobile phones. Overall the perception of AI has been positive with the general public, but there is a high risk of misunderstanding of its abilities and capabilities due to the complexity of the techniques and the fact that the field is still relatively new and frequently improving. The concerns around how we manage AI ethics, safety and legislation together with areas like autonomous weapons put at significant risk the positive perception of AI with the public.

The sectors most, and least likely, to benefit from artificial intelligence

All sectors will benefit from AI, just some will benefit sooner than others. The sectors that will be slower to benefit from AI will be those that are highly manual and / or creative in nature. Will we want to watch a theatre production of Shakespeare’s Hamlet performed by robots. One of the unseen beneficiaries of AI are Charities, as advance data analytics can help a charity better target its services to those in most need, companies like DataKind are facilitating such activities.

The data-based monopolies of some large corporations

While this is a concern, this is already happening without the advent of Artificial Intelligence. From an AI perspective, what we really should be concerned about is that the super intelligence and singularities ALGORITHMS and MODELS are not owned and controlled by a few large non-UK corporations. We need to make sure the UK has a seat at this table and our own talent do not all work for foreign entities. More seed and growth investments for UK AI entrepreneurs is needed. Fundamentally the Algorithms and Models are the engine of machine intelligence, Data is just the fuel.

The ethical implications of artificial intelligence

The transparency of decision making seems to be of importance for the acceptance and adoption of AI in many areas, despite this not being the case for the equivalent human decision makers. This transparency not only relates to being able to audit the internals of the neural network in terms of the features it is using to make a decision, but also to have visibility of the data used to train the system, so that any biases can be seen, or rather it can be demonstrated that any intrinsic biases in the data have been removed or compensated for during the training process. There are other ethical concerns with AI, especially where there is not an obvious right and wrong answer and either outcome has consequences. How can we impose the ethical and moral standards of the user of the AI system rather than rely on the default programmed into the system and thus encapsulating the ethical standards of the coder or the underlying training data.

The role of the Government

There are a number of areas the government can support the adoption of AI and position the UK as the leading authority on AI. Firstly, ensure a strong pipeline of students from school with strong computer science teaching, to university with more courses at undergraduate level and more secondary degree options. Secondly halting the brain drain of researchers and entrepreneur working for US or other non-UK companies. Thirdly, better support for entrepreneurs with seed and growth capital to stop startups being bought by non-UK large corporations. Fourthly, supporting initiatives like Informed.AI who are focused on knowledge sharing and education to ensure a positive perception of AI continues. Fifthly, being ready to support the universal basic salary when AI job displacement reaches levels that require the government to support the general public. Finally, embracing any minor legal changes needed to facilitate the rapid adoption of AI systems.

The work of other countries or international organisations.

Given my Informed.AI platform, that has users from all over the world, I get to see activity from every country. While there are the three obviously epi-centers of activity for AI, namely America, UK & China, there is one other emergent country that is noteworthy, and that is India. A hub of outsourcing IT at the moment, many of India’s leading vendors are building out there AI platforms and capabilities. Informed.AI is an international organisation, with a global team and a number of global initiatives including the meet up chapters of Neurons.AI and the Global Achievement Awards for AI.

22 August 2017

A response to the Science and Technology Committee inquiry into robotics and artificial intelligence

The potential of robotics and automation and the related technologies, namely artificial intelligence and machine learning, supporting the development of these capabilities are significant and it is very encouraging that the government have requested expert opinion about both the impact and opportunities these technologies offer. Acknowledgement must be given to the Robotics and Autonomous Systems (RAS) Special Interest Group, the strategy it published in 2014 and the governments response published in March 2015. The response states actions taken for each of the eight recommendations.

The Science and Technology Committee requested submissions covering the following four areas;

  • The implications of robotics and artificial intelligence on the future UK workforce and job market, and the Government’s preparation for the shift in the UK skills base and training that this may require.
  • The extent to which social and economic opportunities provided by emerging autonomous systems and artificial intelligence technologies are being exploited to deliver benefits to the UK.
  • The extent to which the funding, research and innovation landscape facilitates the UK maintaining a position at the forefront of these technologies, and what measures the Government should take to assist further in these areas.
  • The social, legal and ethical issues raised by developments in robotics and artificial intelligence technologies, and how they should be addressed.

1. The implications for the future UK workforce and job market

One only has to look at the enabling technologies that are developing that impact the taxi business. The first technology was satellite navigation (satnav) which has enabled everyone to have the “knowledge”, the second is the scheduling and booking software, think Uber, which has brought convenience to the process of getting a taxi to the consumer. The third transformational technology is obviously the self-driving cars. Combining all these technologies together, and you have an industry that is completely transformed with the potential to remove the need for taxi drivers from the picture. With this we need to consider where a human driver could add value in the process, for some types of journeys, a chauffeur adds value and prestige. Sometime people need help with there luggage and directions when they arrive at destination. So we need to consider that these technologies are not fully automating the process, but augmenting the process, freeing the driver to focus on more important customer service and value add services.

This is a really important aspect of the transformation that robotics and automation will have on the labour force. In many cases, RAS does not have to fully remove humans from the picture, we embrace the ability for RAS to remove the monotonous tasks from a given job. This is what I refer to as augmentation [1, 2]. Augmentation has the ability for RAS to support the human labour force, allowing the humans to focus on more complex or value add tasks.

We need to educate the population not to fear the advent of technology transformation on the workforce, but to embrace the freedom that it brings. RAS delivers augmentation, which removes the dull repetitive tasks from the job and frees the human to focus on the more interesting, value add, complex tasks.

Given this, many industries will need the workforce to be trained to embrace the changes that RAS technologies will deliver, and understand the changes to the work, focusing more on more complex or value-add tasks.

The other aspect to the workforce and job market, is the need to educate people on how to empower RAS. We need many more people to have a solid understanding of coding. Computer programming is the new language for the 21st century, and we need everyone in the UK to be able to speak it. Coding is important but providing the ability to think in system terms, integrating separate RAS systems to deliver a combined solution will be increasingly important for us all in the near future. There is work done by many companies and initiatives to help educate the workforce, including decoded.com and homeAI.info. These initiatives should be supported more via government grants.

We need to start as early as possible with this skills training. Children are the most receptive to technology and seem to have a natural instinct to adopt them. We should make computer science a foundational subject as much as english and maths is. After all, technology is our future, and those that don’t understand it, and can use it, will get left behind.

It is encouraging that the government via is RAS Challenges is positioning the UK as one of the best places to develop self-drive vehicles, but developing this technology is only part of the story in terms of enabling its adoption, workforce strategy, legal and ethical implications and social factors need to be considered and should be provided for.

While I have used the self-driving cars as an example, it is obvious that the same concerns and factors would be found across many other real applications of RAS.

2. social and economic opportunities exploited to deliver benefits to the UK

The development and commercialisation of RAS technologies has the ability to delight, enthuse and empower a nation. This effect, to be leading the next industrial revolution, should not be understated from both a social and economic perspective.

However, we will need to help the nation make the journey from mystique caused by limited understanding of the technologies to acceptance via information and knowledge of how these systems work. Training and education is a key to the social acceptance of such technologies not just to the ability to create and develop such technologies. If as a nation we can rapidly accept RAS, and accept the changes that augmentation will bring to our lives, we will free ourselves to deliver more economic value to the UK but focusing on more complex and worthwhile endeavours.

With people working less risky and manually intensive jobs (as these will be augmented by robotics and automation), combined with the development of life prolonging methods (medicines, food, age reducing), the population will grow quicker than currently expected and we will have more opportunities for recreational activities to be more frequently available to people. We will have a more utopia outlook, with people working less, living longer and focusing on developing their skills and interests for pure recreational purposes.

3. funding, research and innovation landscape facilitates the UK maintaining a lead position

If the UK wishes to be at the forefront of RAS, then we need to give startups much more support than we currently do. I personally find it frustrating that so many good machine learning startups are being funding or outright purchased by US controlling companies. We need to stop this. Why cant the UK nurture more of its own leaders in this sector. There is no point us having the raw talent and skills needed to provide leading companies in this area, if we then give them up easily to be rolled into the US economy. We should make it a lot easier for startups and developing companies to gain access to funds to continue its research and development activities without the immediate pressures of producing commercial products immediately. We need to support our talent to produce world leading enterprises. The evidence seems that at the moment we are not supporting these companies enough.

While we see that the work Innovate UK and Research Councils, as acknowledged on the commentary on recommendation 2, is bringing together the research and innovation communities by fostering various relationships, there still seems to be a gap from academic activities to commercial enterprise. However, as mentioned with recommendation 5, the cross sector grand challenges, appear to be a perfect platform to develop complex applications of RAS technologies and will help bring the various communities together.

4. the social, legal and ethical issues raised by such technologies

With such a transformational environment, the impact on social, legal and ethical issues are non-trivial. There should be a body established (as there has been in the US) to look at how the technology industry can be supported by considering these aspects. In fact in the US, there have been a number of institutes setup that are concerned with such issues, I am not sure if this fragmentation is helpful. I also believe that there is a real risk of over hyping the social and ethical concerns with such transformation. From the industrial age onwards, we have seen many new technologies introduced into many different sectors, while there may have been some initial resistance, it is difficult to halt the adoption and progress of such. We need to provide the support and guidance that will allow such adoption to be done in a gentle and positive way.

There will no doubt be situations where legal and ethical concerns will need to be addressed, if we don’t act now to start preparations on the legal system to allow the freedom of adoption, the UK will end up being behind other nations. We should continue to have the best legal system in the world that acknowledges and copes for such technology advances. Ethics is an area that has far reaching implications, from the development of the systems, to how they operate, to the bias of any legal system that oversees the implementation of an ethical framework. I would suggest that an ethical framework will be the most difficult aspect, and we have to remember that we don’t have such a framework now, and we instead rely on manufacturers of machines and systems to do the right thing and leverage the legal system to deal with breaches of such trust. So do we really need an ethics framework if the legal system is updated to consider these new technologies.

The social aspects are complex, and span both the changes to the workforce environment to the empowerment that such technologies can give individuals. It will be seen that RAS deliver huge benefits but can, in the wrong hands, deliver devastating results. It must be knowledge that any machine or technology can use applied for good or bad. You only have to think about nuclear fusion for an example of this with the atomic bomb and atomic power stations. Its less about the technology itself but how we decide to apply it. Maybe this leads to a way to licence such technologies for appropriate use, by appropriate bodies, but this may then just force the development of such technologies underground, which would properly be more harmful in the longer term. As part of this we have to ask ourselves now about the implications of autonomous weapons, and who is responsible if such incorrectly targets friends.

summary

In summary, the area of machine learning, robotics and autonomous systems will no-doubt be known as the next industrial revolution, and the UK must be front and centre of this transformation. Establishing a RAS Leadership Council to provide the independent advisory and oversight to deliver the RAS strategy will become increasingly important over the next three to five years.

There are many aspects to consider over and above just facilitating the skills and talent to develop such technologies, we need to be able to navigate the complex ecosystem of research and innovation to empower the transition to the marketplace. We need to support startups and companies so they can remain to deliver economic benefit to the UK rather than see them be transferred to other countries.

Initiatives that look to train people in coding should be supported more via government grants.We should make computer science a foundational subject as much as english and maths is. Training and education is a key to the social acceptance of such technologies

There should be a body established to look at how the technology industry can be supported by considering the social, legal and ethical aspects. We should continue to have the best legal system in the world that acknowledges and copes for such technology advances.

Robotics and Autonomous systems have the potential to deliver huge advantages and benefits to the UK if we are able to accelerate the adoption and leverage the augmentation of such technologies.

Dr Andy Pardoe
Founder of Informed.AI

[1] http://andypardoecom.wpengine.com/blog/the-four-as-pyramid-framework/
[2] http://andypardoecom.wpengine.com/blog/the-four-as-pyramid-framework-part-2/

The Four A’s Pyramid Framework (Part 2)

The Four A’s Pyramid Framework for Artificial Intelligence and Machine Learning (Part 2)

Defining Augmentation – Making the Leap From Analytics to Augmentation

 

Following on from the previous introduction article on the Four ‘A’s Pyramid Framework, we need to determine how we will make the leap from simple analytics to augmentation.

The combination of big data technologies with highly parallel computing power has enabled huge advances is data science, analytics and machine learning. Particularly giving us the ability to better visualise and understand the data being analysed.

Leveraging these advanced analytics including clustering and predictive algorithms on the surface appears to be very easy. There are many platforms now that make the task of training and deploying a predictive model relatively easy. However, while this maybe initially true, this simple setup doesn’t consider a number of important factors that will deliver robust and resilient intelligent systems longer term.

FourAsAugmentation

Lets consider the definition of Augmentation.

Augmentation is about providing the capability to support human activity via computational methods.

This maybe by providing visualisation of information and insight though clustering or ultimately via regression and classification to predict the correct outcome for a given application. Augmentation has the ability to take on the simple tasks previously done by a human, freeing the human to perform the more interesting or complex tasks. There are wider social and workforce implications with this, but they will be covered in other articles. For this article we will focus on the technical aspects.

There are gaps between the platforms and frameworks that are currently available and what is actually needed to provide smart, robust and resilient systems for augmentation.

So what is missing?

Well what we need to understand is that training a predictive model is not a one off task and the data will inevitably change over time and will vary to what the model was trained on. This essentially has the effect of reducing the performance (accuracy) of the model over time. One approach to solve this is obviously to continually train the model, which does make sense and has the potential to deal with varying data over time (active learning). But this itself brings its own set of challenges. Including how do we select the right sample of data to use to train the model. How do we prevent localised skews or abnormalities in the data. How do we ensure that infrequent events are represented and can be part of the generalised model.

In addition we must factor in confidence levels from the model to determine if a specific prediction should be fully automated or needs to be reviewed by the user. The specifics of this will of course vary from application to application, but a standard way to perform this would be useful.

Also another key area that appears to be missing from the current platforms and frameworks is a standard way to feedback from the human as part of this close interplay between the user and the automation that underpins the augmentation. When the human flags a mis-classification, how does the machine learning model factor this into the re-training of the model.

Producing a system that can make accurate predictions only takes us part of the way to delivering a system that can augment tasks successfully over the long term. Any machine learning platform will need to provide algorithms and solutions to the above identified gaps before we can produce robust and resilient intelligent system.

What needs to happen next is to deliver platforms that can augment manual workflow by providing semi-automated systems that support business process and enable the subject matter experts to focus on the more involved and complex elements of the business

While there is a lot of excitement and optimism of what we can achieve with machine learning algorithms and techniques, we need the platforms and integration layers to facilitate a number of capabilities to support augmentation. Identifying these missing capabilities is the first step towards an intelligent system. The next is extending the platforms to deliver these capabilities.

See the introduction to the four A’s pyramid framework

The Four A’s Pyramid Framework

The Four A’s Pyramid Framework for Artificial Intelligence and Machine Learning

Analytics, Augmentation, Automation and Adaptation

 

We need to acknowledge the differences between data science, machine learning and artificial intelligence. By understanding these differences we can clearly define and demonstrate the path from analytics to adaptive intelligence systems. While data analytics can deliver excellent insight into the underlying business dynamics, true artificial intelligence will only be demonstrated when systems can leverage the full suite of techniques and tools of AI within a framework that supports the application of augmentation, automation and finally adaptive methods.

To explore this in more detail, let us try to describe what we mean by the four ‘A’s, however, there needs to be some caution, as we note that each one has some overlap with adjacent layers of the pyramid. The top of the pyramid is Analytics, and is equivalent to the tip of the iceberg. The bottom of the pyramid, is adaptive systems, this being the most sophisticated of the four ‘A’s and the most complex to deliver, but with the most significant benefits. Augmentation and Automation are stepping stones from Analytics to Adaptive Systems, but necessary steps that each can be demonstrated to show benefit to the business while providing the opportunity for the business to gain confidence of the systems put in place.

FourAs

What we are seeing across many industries, is a focus on leveraging big data platforms and data lakes by applying just data science techniques in the form of data analytics. It seems that at the moment, few if any companies realise the power of a platform that not only delivered advanced data analytics and visualisation, but can help them on the journey to produce fully automated and adaptive intelligence systems. Such systems could support entire business processes, freeing up staff to focus on more complex tasks, leaving the automated systems to cover the high volume simpler tasks.

Hopefully some of the companies that are currently building the tools and frameworks that are currently available are planning to deliver more advanced platforms in the future, However, unfortunately at the moment it appears that companies are not producing the platforms and frameworks that are needed to support the development of all four ‘A’s. This seems to be a huge missed opportunity, and should be avoided if we want to prevent the AI field stalling again (avoiding the third AI Winter).

What needs to happen next is to deliver platforms that can augment manual workflow by providing semi-automated systems that support business process and enable the subject matter experts to focus on the more involved and complex elements of the business process. Moving this platform forward to a point where the majority of the process is fully automated would add significant benefit to any business workflow. The bottom layer of the pyramid, is adaptation, and this is where we get fully enabled intelligent systems that can adapt to changes in the underlying data and continue to perform at the level of performance when the system was initially introduced.

I would like to see this pyramid referenced for the development of machine learning frameworks and artificial intelligence platforms, to guide the type of capabilities being build and deployed.

We should not just strive to build data analytics, but produce intelligent systems that can augment and automate business process in a way that adapts to changes in the data space over time providing systems that continue to deliver performant automation.

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