Tag Archives: artificial intelligence

Don’t smash the looms: five reasons why artificial intelligence is nothing to fear

[first published on November 7th 2017 at http://www.thecsuite.co.uk https://www.thecsuite.co.uk/cfo/information-technology-cfo/dont-smash-the-looms-five-reasons-why-artificial-intelligence-is-nothing-to-fear/%5D

Predictions have been rife this year about the threat to jobs from Artificial Intelligence. We are warned that AI will learn how to do our jobs, thus rendering us superfluous. Should we be worried, or do fears about AI demonstrate a failure to learn from history?

The history of technological impacts shows us that fears of job losses from the automation of weaving in the 18th and 19th centuries were unfounded. The Luddites famously smashed automated looms to protest that their craft-based skills were being made redundant and that unemployment and hardship would result. The Luddites believed that technological advancement generates inevitable structural unemployment and is consequently injurious to the macro-economy. The counter-argument is that if a technological innovation results in a reduction of necessary labour inputs in a given sector, then the industry-wide cost of production falls. This, in turn, lowers the competitive price and increases the equilibrium supply point which, theoretically, will require an increase in aggregate labour inputs (Jerome, 1934). Ie the product will become cheaper and more widely available and new demand will be created. This in fact is what happened with woven goods once production was automated. The sale of rugs and carpets and a myriad of other woven products expanded enormously and a cottage industry that employed a few thousand craft people weaving by hand was joined by a huge industry employing hundreds of thousands of people in producing, storing, transporting, and selling similar goods produced by machines. Though the immediate fears of the Luddites were understandable, so incorrect were their predictions that economists coined the phrase ‘Luddite Phallacy’ to belittle any further claims that new technology would result in net job losses. And note that the craft-based industry of the Luddites was not replaced by the automated industry. Their work continued to this day where it is treated as the luxury product that it is.

By now, we 21st century people should be confident that any new technology will not create mass unemployment but will instead create jobs and boost economies. And yet, our fears remain. Fears that are stoked by the media (whose interest is not in the welfare of workers but in their ability to sell newspapers, subscriptions and advertising in print, online, and on TV.).

Five reasons we shouldn’t fear AI:

1. AI does not do the work that people do; it does the work that people cannot do

One mistake that people make when debating AI is to assume that it does work currently being done by humans. By and large, it does not. Instead, it does work that people cannot do at all, or cannot do easily, or cannot do sufficiently well in a reasonable timescale. Or it does work that is being done by machines already, but it does it much better than existing machines.

Previously, people have used digital calculators, spreadsheets and computer modeling techniques to do many of the things that they could now (or in the future) use AI to do faster and better. Those same people can now use AI techniques such as pattern recognition to meta-analyse Big Data from an infinite number of sources. An example of a machine driven process being replaced by a better (AI) machine is robo-advisory investment management services for retail customers. Current algorithm driven methods via online services have a reputation for being clunky and simplistic. AI transforms this service with a level of sophistication that exceeds enormously what algorithms can do. Result: no human replaced, but many happy humans as result.

AI is an additive technology that opens up a whole new world of possibility to government, science, medicine, technology, logistics, education, and commerce. Through AI techniques of natural language processing, machine learning, deep learning, and cognitive computing, people and organisations can better automate processes, gain non-intuitive insights into data, and manufacture ‘better things better’. Non-intuitive insights from data can generate and validate new economic, business and investment strategies. In capital and commodity markets, the more efficient use of capital afforded by using AI tools can provide huge stimuli to economies through increased capital for investment.

2. AI does not destroy jobs; it creates a huge number of jobs

Rather than causing unemployment, factories created millions of jobs in the 18th and 19th centuries. AI unleashes human potential to do more, bigger, faster and better. It allows us the ability to do the things we always wanted to do, plus a lot more things we haven’t yet considered. That is how jobs are created. Already, AI has created many more jobs than it has ever replaced. Constellation Research predicts that the market for AI will be worth $100 billion by 2020. Many of the jobs being created by AI are jobs that (could) never existed before.

3. AI creates jobs not just in its own development, but in every industry that uses it

As well as ‘pure AI’ roles there are many more jobs available in industries that are using AI to do new things, or do old things better — and in the process creating increased demand and increased job numbers. One example is cyber-security. Cyber security uses a wide range of AI approaches and techniques to keep our data, identities and money safe. These include machine learning, pattern recognition, and fuzzy logic. And yet there is a huge skill gap such that firms struggle to fill open positions. The ISACA (a non-profit information security advocacy group), predicts there will be a global shortage of two million cyber security professionals by 2019. In financial services and capital markets, AI is the science behind anti-money laundering processes and technologies as well as many other forms of risk management including ‘Regtech’, the AI based technology used to assure regulatory compliance by making sense of multiple — often conflicting or incomplete — data sources.

4. AI will not kill us. AI will save us

Rather than worrying about something that will never happen (eg autonomous robots wiping us from the face of the earth) we should focus on how many lives are being saved right now by the use of AI in medicine and surgery. Or we should think about how many hungry mouths are being fed more cheaply by improved agriculture coming from AI techniques and technologies. Or how AI is protecting your online identity and the data in our banks.

5. We will never be ready

Were we ready for the internet — part of the third industrial revolution — and everything (good and bad) that it brought us? Of course not, because we couldn’t predict the new business models that would be facilitated by such a technology, having never seen its like.

Final thoughts: The irony of change management in many organisations is that it ensures that real change never happens, because real change cannot be ‘managed’. Real change is almost always a reaction to significant change in the environment — including opportunities and threats created by new business models enabled by new technologies. Who among us can really predict everything that a large number of new technologies arriving at once could generate? Technologies such as nanotechnology; atomically precise engineering; conscious technology; a hyper-connected (and thus arguably conscious) internet of humanity; mixed reality living; synthetic biology; human augmentation; brain uploading; internet of everything; and, AI, to name but a few. None of us can come close to fully imagining all the new business, social and environmental models and opportunities that will be created by these technologies, but we can be sure of one thing: they will create huge numbers of jobs and businesses world-wide. They always do. We should be grateful for that. Please don’t smash the looms — but more importantly, please don’t be scared of them.

Cliff Moyce

Originally published at http://www.thecsuite.co.uk.

Cognitive analytics gives business the edge

Monday, 10 October 2016

The cognitive analytics revolution in business is underway. It is underpinned by artificial intelligence, cognitive computing and machine learning. Cognitive analytics will give business executives such as the CEO, CFO, CIO and CMO massively enhanced data- driven decision-making abilities, as well as the ability to track and learn from prior decisions. The change means that decisions can be informed by non-intuitive insights on products, services, business operations and markets (including client behaviours) drawn from a wide variety of sources. Those sources will include unstructured data such as social media posts, images, and academic documents. We have seen already how the ability to do post-hoc analyses of the economic, political and legal decisions of governments and legislatures can generate non-intuitive insights unavailable through traditional methods; now it is time for the boardroom to be doing the same.

It almost goes without saying that the use of the word ‘cognitive’ implies the continued quest in computing to create intelligent business machines that operate as per the human brain, “by reverse engineering the computational function of the brain” (Modha, D.S., 2011). Combining neural models and technologies with huge processing power can take us well beyond what any of us could achieve alone or in teams, even huge teams, with current analysis tools and techniques.

The way that cognitive analytics achieves its magic over and above current data analysis methods is through (1) ability to analyse huge amounts of unstructured data alongside traditional structured data sets; (2) ability of cognitive analytics tools to generate non-intuitive insights from data; and (3) ability for the tools to learn as they work – including how decisions suggested by the tool previously panned out when implemented (post-hoc analyses). Unstructured data that are handled well by cognitive analytics tools include emails, videos, documents, images, social media posts, academic articles etc. Cognitive computing uses natural language processing, probabilistic reasoning, machine learning and other technologies and techniques to analyse content efficiently; analyse context; and, find near real-time insights and answers hidden within massive amounts of information. Cognitive systems can adapt and get smarter over time by learning through their interactions with data and through human decision-making (including decisions suggested by the same cognitive systems). Insights provided through cognitive analytics will focus us more on the questions that we ask. These insights can help break us free from the prisons of wrong assumptions, faulty hypotheses, and the tendency to confuse symptoms with causes.

All areas of business can be supported and enhanced by cognitive analytics. These include business strategy (for example, mergers and acquisitions); product design and marketing; financial planning (from capital planning to cash management to financial control); and business operations (eg the efficient and effective deployment of resources for maximum productivity).

Financial services and capital markets have been using a form of algorithmic artificial intelligence methods for some time. Eg algorithmic trading methods using machine-learning and ‘cognitive’ (ie loosely coupled) logic to make decisions; and, predictive / trends / risk and behavioural analyses using similar methods for financial crime prevention.  Those algorithmic cognitive or quasi-cognitive approaches are also seen in wealth management ‘robo-advisory’ offerings, and will start to be seen more generally in digital banking.  In the finance function we have forecasting systems that use online analytical processing (OLAP). We also see algorithmic predictive analyses in cashflow forecasting and demand planning. What ‘real’ (ie based on neural models) cognitive analytics will give finance and business planning functions is the ability to use many data types that cannot be analysed easily currently; further and better analyses of the huge amounts of data held by the function; and, the ability to derive non-intuitive insights from data that are not being derived currently. This step-change in capability will strengthen the ability of those functions to add value to strategic and operational planning. Eg in financial control, cognitive analytics can (relatively pro-actively) highlight problems, or areas for optimisation. It can also track in real time or monitor retrospectively actual performance against financial plans, and provide feedback that companies can use to fine-tune their planning approaches. In fact, if a toolset is genuinely cognitive it should learn to fine-tune approaches itself. Similarly, the ability of marketing and product development teams to better predict consumer behaviours will reduce the risk of product failure as well as driving innovation that may not have occurred otherwise.

In summary, cognitive analytics is set to transform our ability to plan, develop and run businesses. It is genuinely transformational. Though it is not a panacea for all ills, it will help enormously with diagnosing those ills. Early adopters will be well rewarded.

Cliff Moyce

[first published at ftseglobalmarekts.com on 10 October 2016]

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