Mr Brooks Artificial Intelligence, Economics 0 Comment

 

In the dynamic panorama of technological evolution, the confluence of blockchain technology and Artificial Intelligence (AI) stands as a beacon of transformative potential, reminiscent of the profound metamorphoses witnessed in the annals of economic history. This blog endeavors to weave through the intricate narrative of how this symbiotic relationship between blockchain and AI could herald a paradigm shift across various sectors of our economy, mirroring the revolutionary spirit of yesteryears’ technological leaps.

Blockchain, with its immutable ledger and decentralized ethos, offers a foundation of trust and transparency that is unparalleled in the digital domain. When harmonized with AI’s analytical prowess and predictive capabilities, the duo embarks on a journey to redefine the infrastructure of economic transactions and interactions. The amalgamation of blockchain’s integrity with AI’s cognitive acumen paves the way for a new epoch of efficiency, security, and innovation in economic practices.

One of the quintessential arenas where this fusion promises to make significant inroads is in the realm of financial services. Blockchain’s incorruptible ledger, combined with AI’s ability to sift through and analyze vast datasets, can revolutionize how we perceive banking, lending, and investment. This integration not only augments the precision of financial forecasting but also enhances fraud detection mechanisms, thereby fortifying the bedrock of financial stability and trust.

Moreover, the synergy of blockchain and AI holds the potential to streamline supply chain management, transforming it into a paragon of efficiency and transparency. Blockchain’s ability to provide a tamper-proof record of transactions, coupled with AI’s capability to optimize logistics and predict market demands, could significantly reduce inefficiencies, mitigate risks, and foster a more sustainable economic ecosystem.

In the broader spectrum, this confluence could also catalyze advancements in areas such as healthcare, where secure and verifiable exchange of medical data, powered by AI-driven insights, could elevate patient care to unprecedented levels. Similarly, in the energy sector, blockchain and AI could facilitate the transition towards more decentralized and efficient energy systems, optimizing resource allocation and consumption patterns.

However, as we navigate through the labyrinth of possibilities presented by the integration of blockchain and AI, it behooves us to tread with caution and foresight. The ethical considerations, regulatory frameworks, and potential socio-economic impacts warrant a nuanced discourse to ensure that this technological amalgamation serves the greater good and mitigates unintended consequences.

In conclusion, the fusion of blockchain technology with AI represents a frontier brimming with potential to revolutionize myriad facets of our economy. It beckons us to reimagine the fabric of economic transactions and interactions, promising a future that is more resilient, equitable, and innovative. As we stand at the cusp of this exciting venture, the question that looms large is: How will we steer this confluence of technological marvels to sculpt an economic landscape that not only thrives on innovation but also upholds the tenets of ethical and equitable growth?

Charting the Future: Navigating Economic Horizons with AI

Mr Brooks Artificial Intelligence, Economics 0 Comment

In the ever-evolving landscape of economics, the introduction of Artificial Intelligence (AI) has sparked a revolution akin to the advent of the steam engine during the Industrial Revolution. The realm of economic forecasting, traditionally a domain where human intuition and experience played pivotal roles, is now witnessing a seismic shift towards data-driven precision, thanks to AI’s prowess. This blog aims to unravel the intricate tapestry of AI’s role in economic forecasting, navigating through its promises, challenges, and the profound implications it holds for the future.

AI, with its formidable computational abilities, has the potential to dissect and analyze vast oceans of data, identifying patterns and trends that the human eye might overlook. In the context of economic forecasting, this translates into a more nuanced understanding of market dynamics, consumer behavior, and the intricate web of global economic interactions. The precision and depth of insight offered by AI can significantly enhance the accuracy of forecasts, providing policymakers and businesses with a more reliable compass to navigate the uncertain waters of the global economy.

However, the integration of AI into economic forecasting is not without its challenges. The complexity and often opaque nature of AI algorithms can lead to a ‘black box’ scenario, where the rationale behind forecasts becomes enigmatic. This opacity can undermine trust in AI-generated insights, particularly in a field as consequential as economics, where transparency and accountability are paramount.

Moreover, the reliance on historical data by AI algorithms raises questions about their ability to anticipate unprecedented events or economic shocks. The global financial crisis of 2008 and the recent pandemic-induced economic upheavals serve as stark reminders of the unpredictable nature of economic systems. Can AI, tethered as it is to the patterns of the past, truly foresee the novelties of the future?

Despite these challenges, the potential of AI in transforming economic forecasting is undeniable. As AI technologies evolve and become more sophisticated, their ability to incorporate a wider array of variables, including those reflecting human behavior and sentiment, will likely increase. This evolution could pave the way for forecasts that are not only more accurate but also more attuned to the complexities of human economic activity.

In conclusion, the fusion of AI with economic forecasting heralds a new era of data-driven decision-making. While challenges abound, the journey towards harnessing AI’s full potential in this domain is fraught with opportunities to redefine how we understand and predict economic phenomena. As we stand on the brink of this exciting frontier, one thing is clear: the synergy between AI and economic forecasting will undoubtedly shape the contours of our economic future. The question remains, however, as to how we navigate this confluence of human intelligence and artificial prowess to foster an economic landscape that is resilient, equitable, and thriving.

Should we embrace AI in education? Is it even a question?

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It’s been a full five years since I last updated this page, and I must say, it’s pretty absurd to even question whether we should embrace the use of AI in education. But, I suppose, in the spirit of fairness, I’ll attempt to present both sides of the argument before I inevitably try to convince you of why we should absolutely be incorporating AI in our classrooms.

First, let’s examine the naysayers. There are those who worry that AI will replace human teachers, leading to a loss of jobs and a further dehumanization of education. Some also express concern over privacy and the possibility of AI algorithms perpetuating biases. And, of course, there’s always the fear of the unknown and the uncertainty that comes with incorporating new technology.

While these are all valid concerns, I would argue that they are outweighed by the numerous benefits that AI brings to education. For starters, AI has the potential to personalize learning and provide individualized feedback to students, something that is difficult for human teachers to do in a classroom of 30 or more students. This can result in increased engagement and motivation for students, as well as improved outcomes.

AI can also help educators save time and effort by automating tasks such as grading, freeing up more time for teachers to focus on teaching and developing relationships with students. Additionally, AI can provide valuable insights into student performance, allowing teachers to identify areas where they need to focus their efforts and make data-driven decisions.

Another advantage of AI in education is that it can help to level the playing field for students, especially those who are economically disadvantaged or who have disabilities. With AI, these students have access to educational resources that they might not have had before, as well as tools that can help them to overcome their learning challenges.

But let’s not forget, AI is not a silver bullet and there are limitations to its use in education. For one, AI algorithms can only do what they were designed to do, and they may not always be able to understand the nuances of human language and thought. Additionally, AI can only provide feedback and support within the parameters set by its designers, and it may not always be able to adapt to the needs of individual students.

So, while there are certainly concerns to be addressed, I believe that the benefits of incorporating AI in education far outweigh any potential downsides. With the right balance of human and machine, we can create a more personalized and effective learning experience for students, as well as help to level the playing field for those who have historically been disadvantaged.

In conclusion, it’s time for us to stop questioning whether we should embrace AI in education and start figuring out how we can effectively incorporate it into our classrooms. The future of education is already here, and the choice is ours whether we want to be left behind or become leaders in this new era of education. So, let’s get to work!

Brought to you by Chatty G and Dalle.

Working conditions in factories in industrial Britain

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The shift from working at home to working in factories in the early 18th century brought with it a new system of working. Factory and mine owners sought to control and discipline their workforce through a system of long working hours, fines and low wages. The conditions in factories in industrial Britain were harsh and included:

  • Long working hours: normal shifts were usually 12-14 hours a day, with extra time required during busy periods. Workers were often required to clean their machines during their mealtimes. (The 10 hours act 1847 restricted the amount of hours women and children under the age of 17 could work)
  • Low wages: a typical wage for male workers was about 15 shillings (75p) a week, but women and children were paid much less, with women earning seven shillings (35p) and children three shillings (15p). For this reason, employers preferred to employ women and children. Many men were sacked when they reached adulthood; then they had to be supported by their wives and children.
  • Cruel discipline: there was frequent strapping (hitting with a leather strap). Other punishments included hanging iron weights around children’s necks, hanging them from the roof in baskets, nailing children’s ears to the table, and dowsing them in water butts to keep them awake.
  • Fierce systems of fines: these were imposed for talking or whistling, leaving the room without permission, or having a little dirt on a machine. It was claimed that employers altered the time on the clocks to make their workers late so that they could fine them. Some employers demanded that their overseers raise a minimum amount each week from fines.
  • Accidents: forcing children to crawl into dangerous, unguarded machinery led to many accidents. Up to 40 per cent of accident cases at Manchester Infirmary in 1833 were factory accidents.
  • Health: cotton thread had to be spun in damp, warm conditions. Going straight out into the cold night air led to many cases of pneumonia. The air was full of dust, which led to chest and lung diseases and loud noise made by machines damaged workers’ hearing.
  • Parish apprentices: orphans from workhouses in southern England were “apprenticed” to factory owners, supposedly to learn the textiles trade. They worked 12-hour shifts, and slept in barracks attached to the factory in beds just vacated by children about to start the next shift.

Restrictions did come in with the Factory Act of 1833 which restricted the age at which children could work, there was  further restrictions with the 10 Hours Act 1847 which meant the amount of hours women and children under the age of 17 could work were reduced.

(adapted from BBC Bitesize)

 

The Economic Problem

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How to Think Like an Economist

Every new subject requires new patterns of thought; every intellectual discipline calls for new ways of thinking about the world. After all, that is what makes it a discipline: a discipline that allows people to think about a subject in some new way. Economics is no exception.

In a way, learning an intellectual discipline like economics is similar to learning a new language or being initiated into a club. Economists’ way of thinking allows us to see the economy more sharply and clearly than we could in other ways. (Of course, it can also cause us to miss certain relationships that are hard to quantify or hard to think of as purchases and sales; that is why economics is not the only social science, and we need sociologists, political scientists, historians, psychologists, and anthropologists as well.) In this chapter we will survey the intellectual landmarks of economists’ system of thought, in order to help you orient yourself in the mental landscape of economics.

 

Economics: What Kind of Discipline Is It?

If you are coming to economics from a background in the natural sciences, you probably expect economics to be something like a natural science, only less so: You probably think that to the extent that it works, it works more or less like chemistry, though it does not work as well. It does not work as well because economic theories are unsettled and poorly described. It does not work as well because economists’ predictions are often wrong.

If you are coming to economics from a background in the humanities, you probably see it as a combination of two centuries out-of-date psychology and moral philosophy, coupled with obscure and often wrong—yet somehow authoritative in some way—mathematical manipulations.

If you hold either of these opinions, you are half-right.

While economics is not a natural science, it is a science—a social science. Its subject is not electrons or elements but human beings: people and how they behave. This makes it something more and something more difficult than it would be if it were just like a natural science but underdeveloped and badly done. The fact that economics’ subject matter is people has important consequences, that make economics easier than a natural science in some ways, harder than a natural science in other ways, and in yet other ways just plain different. Moreover, economics is an inherently quantitative social science. And it has developed as an abstract social science.

While economics is not a humanity, it is humanistic. Its subject matter is made up not of quarks or molecules or animals but of people. And to understand people you have to get inside their heads: understand their hopes, fears, desires, reasoning, plans, expectations, and actions. Thus one of the principal intellectual moves in economics is one that is totally absent from the natural sciences: it is for you to imagine yourself in the place of the people you are studying. Thus economics often turns into an exercise in introspective psychology.

A Social Science: The principal things to remember that flow because economics is a social and not a natural science are:

  1. Because economics is a social science, debates within economics last a lot longer and are much less likely to end in a clear consensus than are debates in the natural sciences. The major reason is that different people have different views of what makes a free, a good, a just, or a well-ordered society. They look for an economy that harmonizes with their vision of what a society should be. They ignore or explain away facts that turn out to be inconvenient for their particular political views. People are, after all, only human.

    Economists tryto approach the objectivity that characterizes most work in the natural sciences. After all, what is is, and what is not is not. Even if wishful thinking or predispositions contaminate the results of a single study, later studies can correct the error. But economists never approach the unanimity with which physicists embraced the theory of relativity, chemists embraced the oxygen theory of combustion, and biologists rejected the Lamarckian inheritance of acquired characteristics. Biology departments do not have Lamarckians. Chemistry departments do not have phlogistonists. But economics departments do have a wide variety of points of view and schools of thought.

  2. That economics is about people means that economists cannot ethically undertake large-scale experiments. Economists cannot set up special situations in which potential sources of disturbance are reduced to a minimum, then observe what happens, and generalize from the results of the experiment (where sources of disturbance are absent) to what happens in the world (where sources of disturbance are common). Thus the experimental method, the driver of rapid progress in many of the natural sciences, is lacking in economics. This flaw makes economics harder to analyze, and it makes economists’ conclusions much more tentative and subject to dispute, than is the case with natural sciences.
  3. That economics studies people means that the subjects economists study have minds of their own. They observe what is going on around them, plan for the future, and take steps to avoid future consequences that they foresee and fear will be unpleasant. At times they simply do what they want, just because they feel like doing it. Thus in economists’ analyses the present often depends not just on the past but on the future as well—or on what people expect the future to be. Box 3.1 presents one example of this: how people’s expectations of the future and particularly their fear that there might be a depression contributed to the coming of the Great Depression of the 1930s.

    This third wrinkle makes economics in some sense very hard. Natural scientists can always assume the arrow of causality points from the past to the future. In economics people’s expectations of the future mean that the arrow of causality often points the other way, from the (anticipated) future back to the present, so that the effect of future (expected) economic events and processes can be to “cause” things in the past—which then cause the future. And things can get very weird indeed.

Human Resources Group Activities

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Activity 1

Image by Christian Weidinger

An HR department in a chain of cycle shops is dealing with an unexpected fall in sales of bicycles in the summer of 2015. After analysing external data plus data from the marketing and sales department, the potential of bike sales in 5 years time, suggested the trend would continue. HR must advise leaders on staff deployment and strategies with this information in mind, What recommendations should HR be making to leaders to make sure the business is healthy over the next few years?

How might the current situation affect the Cycle chains’ succession planning?

 

Activity 2

The HR department for a small chain of office supply stores has been analysing internal data and  market intelligence, the data shows that the market have been very uneven and is very hard to predict when the busy periods for the store are going to be and when there will be quieter periods. Market intelligence suggests that there is no reason to say that the situation will change anytime soon due to the uncertain economic climate. HR must advise leaders on staff deployment and strategies, with this information in mind, what recommendations should HR be making to leaders to make sure the business is healthy over the next few years?

How might the current situation affect the office supply chains’ succession planning?

 

Activity 3

The HR department for a chain of kitchen showrooms has been accustomed over the years to regular high volume sales in the run up to Christmas and the run up to summer. They noticed through internal data that the last two years has seen the trading cycles lengthen, with high volume sales earlier in both the run up to Christmas and summer, with higher volumes spread at various times throughout the year as well. Market intelligence suggests that this trend may well continue with the public choosing to spend on their existing homes rather than moving due to economic uncertainty. HR must advise leaders on staff deployment and strategies, with this information in mind, what recommendations should HR be making to leaders to make sure the business is healthy over the next few years?

How might the current situation affect the retail kitchen chains’ succession planning?

Human resources as a factor of production

Mr Brooks Uncategorized , , , 1 Comment

Image by David Woo

‘Resource is a term used in Economics, and human resources as a factor of production relates to the resources required to produce goods or deliver services. Resources can be both human and physical and include equipment, technology etc.

 

The HR department is involved in managing physical resources by monitoring the effectiveness of teams and individuals, involving human managers primarily. For example, managers are directly responsible for their teams and therefore any requests for increased resources usually require approval from HR. HR will either have a budget or monitor managers’ spending against the budget for resourcing.

 

Take an example when a member of staff is on extended sick leave, resulting in a department being under resourced to meet deadlines and demands. In some businesses mangers will have the authority to take on a temporary member of staff to cover the period needed although the HR department is likely to have a preferred list of agencies to use. In other businesses, possibly due to the size of the organisations, the request will have to go to HR for them to deal with, although the manager may be involved in selecting the right individual, or following an initial screening by HR.

 

HR departments will use various forms of information to make decisions about the suitability of the resources and advise leaders and managers on whether or not departments should be restructured.’

 

Source: Pearson BTEC Level 3 National Extended Diploma in Business (2016)

NMC Horizon Studies

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Photo: Courtesy my.opera.com

On reading the 2015 NMC Horizon study it seemed to me to be a sound assessment of the way technologies are heading in education but some areas seemed to lack ambition. This could obviously be down to the fact that education is well known to be slow on the uptake of new technologies, and the writers did not have the luxury of hindsight. One area which struck me was adaptive learning technologies which in the 2015 report it was pitched at 4 to 5 years and by the 2017 report is a year or less, these technologies in some way are already being used in education, in this way I think the report was being conservative, much like education itself. The internet of things was part of the 2015 report and again in 2017, on the same timeline moving from 4-5 years to 2-3 years. This one is very hard to gauge in my opinion as the term encompasses such a broad area who is to say when the uptake of these technologies really happen, some institutions would argue now Virginia Tech and The University of New South Wales for example, as mentioned in the report. I would imagine we will see a slow proliferation of these technologies making their way into institutions.

A couple of technologies that really interested me from the 2017 report was Artificial intelligence and next generation VLE’s, these are both areas that have the capacity to have a profound effect on education and these are areas that could well work together. There are already applications coming to market that use AI capability one such application is seeing AI by Microsoft, this application uses the camera on a phone and describes the world around it. It is described as being designed for the low vision community. The adoption timeline for the next generation VLE’s is 2-3 years and Artificial intelligence 4-5 years, if the desire is there I’m sure these timelines could be on the pessimistic side as the technologies are in place already, the need is definitely there in both areas especially as there is a general acceptance that students need a more personalised learning experience.

A technology that is missing from the reports in my opinion, and a technology that I have spoken about at some length is blockchain technology. This could have a profound effect on education and specifically the measurement of achievement and dissemination of research (a discussion of the form in which this may take place this can be found at http://cloudworks.ac.uk/cloud/view/10187). A site called steemit came on the scene in 2016 that also utilises blockchain technology and encourages users to post content and in return are given tokens, depending on the popularity of the piece, each post can be upvoted, so more votes, more rewards

With regards to measuring achievement a platform is being developed at appii.io that is utilising blockchain technology to securely keep records of achievement but also connect students and employers to maximise student’s strengths and preferences with employer needs. I am surprised that blockchain technology has never made it into the report, the authors may have their reasons for this but in my opinion this technology will change the educational landscape and beyond in profound ways and I would place this at the mid term of 2-3 years.
Am I too optimistic?

Already on the PLE Highway?

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I don’t think it would be too controversial to say that the use of some kind of PLE in education is becoming more commonplace as people start to make use of technologies in various areas of their life. Where is this all heading? If we can accept that students learn in different ways and what is good for one student may not be good for another then we have to accept that a one size fits all system is not optimum either. As Weller points out in the Sclater/Weller (2016) podcast that students do not want to use an inferior system in their studies than they are using in their leisure time, they’re are some great free tools out there that work very well, but as Sclater counters that students need to be able to interact so they must be using systems that allow this, although it is a red herring to suggest that a good reason for not partaking in the use of different technologies in the form of a PLE is in case of technical failure, of course any platform can fail but if your environment is distributed then it is much less likely your whole environment is down at the same time. It is true that there needs to be some consistency for ease of communication, but at the same time students needs and preferences need to be taken into account for optimum learning potential, after all there are a vast range of technologies waiting to be exploited, along with a vast range of preferences. How would this work? Some kind of hybrid system? Maybe it is fair comment from sclater to suggest that far from the VLE being put out to pasture it could become more of a back end system. This is the direction I see for the PLE. We do need to give students ownership over the tools they use if they are to get the most out of their learning as they possibly can, but their needs to be a route for the students to maintain fluid communication. This currently does not happen with the VLE either, forums are not instant or fluid enough and the whole system is a little restrictive when compared to the tools that can be utilised in everyday life. What would be better and a potential direction is for students to choose the tools that they wish to use and have a system that interacts with these platforms giving students the flexibility of using their PLE whilst keeping the ability to communicate, of course there may be situations that need to be kept in a closed forum or at least desirable, but a more flexible system is surely more beneficial for all. Is this fanciful? Is education heading down the PLE highway? Its already on it, where to next?

Is technology itself a cause of reform in education or an instrument used to encourage reform?

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As we have seen, technology has been making massive waves in recent years in education and the impact can be seen in the ways in which courses are delivered and also in the way students are interacting with their courses, this can be seen both in the class and on distance learning courses. Education itself is notoriously slow on the uptake of new technologies but of course these technologies are no longer new and the people that are innovating have grown up with these technologies. Chris Jones asks the above question in the context of an activity as part of an MA module. In my view, technology can not cause reform in itself as people need to be willing to use the technology and see the value in its use. In my opinion though it is being used as an instrument of reform, just look at the explosion of MOOCs the sheer number of courses available online now is incredible and the surge in uptake of these courses is equally phenomenal. According to class-central.com the number of students has more than doubled in the last year up from 16-18 million to over 35 million   ( https://www.class-central.com/report/moocs-2015-stats/) These are obviously headline figures and the real story is a lot more nuanced than this, for example are these students that are registered to a platform, or registered to a course, have started a course, partially finished or finished a course, the permutations are endless, but it is plain to see that the interest in massive online courses is growing at a rapid rate. This is a massive subject in itself and is in danger of spinning out of control just like the MOOC figures, so to come back to the original question, I think it is reasonable to say that, yes, technology has been an instrument used to encourage reform, after all the big players in this field aren’t creating all this content out of a sense of social justice or just because they can. This is becoming a very big market as people are changing their learning habits, and see the MOOC model as one that is very flexible and one that they can jump in and out of as they see fit. People obviously have different reasons for wanting to learn but the point is that with the MOOC model, the previous barriers of large time and financial commitments have been lowered a great deal and in many cases the financial commitment has been completely removed. Society at large is changing continuously and education is and must change also.