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IS BITCOIN SET TO HAVE A 2017-STYLE MINI BOOM THIS YEAR?

Bitcoin’s price is set to “surge before the end of 2020” with investors keen not to “sleepwalk” through a 2017-style mini-boom, says the CEO of one of the world’s largest independent financial advisory and fintech organizations.

The prediction from Nigel Green, the deVere Group CEO and founder, which has $12bn under advisement, comes as Bitcoin – already one of the best-performing assets this year – appears to be on the brink of a bullish breakout.

In recent days, Square, which is owned by the billionaire founders of Twitter, has allocated 1% of its cash reserves to the cryptocurrency, whilst a former Goldman Sachs hedge fund chief says the price of Bitcoin will jump to $1m in five years.

Mr Green comments: “There’s been something of an avalanche of interest in Bitcoin in recent weeks from household-name investors.

“Investor activity is picking up considerably with various on-chain metrics and ongoing – and heightening – global political, economic and social turbulence suggesting that there will be a price surge before the end of the year.

“Like gold, Bitcoin can be expected to retain its value or even grow in value when other assets fall, therefore enabling investors to reduce their exposure to losses.
“Investors will increase exposure to decentralised, non-sovereign, secure digital currencies, such as Bitcoin, to help shield them from the potential issues in traditional markets”.

He continues: “There’s a growing sense that we’re set to experience a mini-boom similar to that at the end of 2017.

“Prices are yet to catch-up with investor interest – but this is only a matter of time as investors will not want to sleepwalk towards perhaps year-high prices in the run-up to the end of 2020.”

The late 2017 bull run saw the Bitcoin price reach its all-time high of $20,089.

The deVere CEO concludes: “There’s been a notable ramping-up of interest in Bitcoin amongst investors since the end of summer. Indeed, it has been the best performing week for one of the year’s best-performing assets since July.

“I can see no reason why this upward trajectory will not continue between now and the end of the year.”

 

Technology

USING ARTIFICIAL INTELLIGENCE TO ACHIEVE CIRCULAR ECONOMY

By Professor Terence Tse, ESCP Business School

 

It is really only a matter of time before the two main trends, artificial intelligence (AI) and circular economy, would come together. A milestone of this convergence was the white paper “Artificial intelligence and the circular economy”: AI as a tool to accelerate the transition, jointly published by The Ellen MacArthur Foundation and Google earlier this year. It has kick-started the discussion on how AI can be used as a tool to help accelerate and scale our transition to a circular economy. This can be achieved by unlocking new opportunities through improving product and material design, enhancing circularity-based business models, and optimising circular infrastructure. The paper draws on the food and consumer electronics industries to illustrate the circular benefits driven by AI. The forecasted value that can emerge from these is encouraging: up to $127 billion and $90 billion a year in 2030, respectively.

 

The pace will be slow

No doubt these are very good news. It also shows how innovative technologies can take circular economy to the next level. Yet, I believe the path leading there will be full of challenges, not least because, contrary to what general media would like to get us to believe, the development of AI is, in reality, really slow.

 

There are several reasons attributable to this sluggish pace

First, there is a general shortage of AI-proficient graduates. Training up AI researchers takes time. Universities are not churning out data scientists fast enough to meet the job market demand. For those who are graduating, they will most likely be snapped up by the technology giants. Indeed, it has been estimated that some 60% of AI talent are in the employment of technology and financial services companies, leading to a ‘brain drain’ in academia, which in turn, slows down the production of qualified graduates. Small circular economy-based companies (as well as AI start-ups) will struggle to have the same hiring power, as they often lack the ability to match the levels of salaries and prestige offered by large organisations.

Another reason why circular economy-aimed companies, large or small, will struggle to deploy AI is that the technology remains a very expensive investment. AI is, at the moment, far from a plug-and-play technology. Arguably, there are off-the-shelf AI applications available in the market. But what this one size fits-all technology solutions can really do is often very limited and their effectiveness low. Inevitably, for AI to work at an acceptable, value-creating level, it is necessary to integrate it into the existing wider IT system. Customising AI applications to be embedded in the system architecture is very complex and hence very costly.

To make matters worse, the market is seemingly inundated with self-proclaimed AI companies. A recent report has suggested that 40 percent of start-ups in Europe that are classified as AI companies do not actually use artificial intelligence technologies in a way that is “material” to their businesses. As someone who researches and works in the business of AI, I can readily observe this phenomenon has already eroded the trust of many companies, making them increasingly cautious when proceeding with investment and deployment of AI.

 

Gradual developments, not quantum jump

For these reasons above, the adoption of AI, and by extension, in the area of circular economy, will be slow. This, however, does not mean there will be no advancement. Instead of “big bang” new business model creations, AI will most likely produce circular advantages through baby steps in operational enhancement gradually. For instance, one of the important elements in achieving circular economy is better asset management. In a recent research project for the European Defence Agency, my colleagues and I have discovered that there is a wide spectrum of operations for ministries of defence to save money and practise circular economy, from refurbishing and repurposing small military equipment items to reduce waste and minimise the use of virgin materials to extending the service years of capital assets. Unquestionably, the same may be applied to civilian activities. For example, combining the power of AI and drones can extend the longevity of major infrastructure such as reactors and bridges.

Advancements in drone technologies have allowed them to be deployed to take pictures at heights that are dangerous for inspectors to reach. The contributions of AI come from its ability to analyse and identify cracks as well as defects on assets that are not always visible to human eyes from captured images. Consequently, problems are detected before the assets become irreparable, thereby lengthening their lifetime.

A seemingly insignificant but potentially huge possibility of waste reduction would be saving on paper use. In the insurance industry, for instance, there is still a huge reliance on actual paper, with the communications between various stakeholders, including the underwriters, brokers and insured, passing on a large number of physical documents. AI techniques, in particular natural language processing, can help speed up the digitalisation of documents as they can go beyond the point of just reading and processing text to recognising and recording signatures and rubber stamp marks. Little by little, it will be possible to lower paper consumption.

 

The future is now

Both AI and circular economy are by themselves breakthrough ideas that are set to change the world dramatically. Combined, it can be a very powerful force of good. But this can only be achieved if we can synthesise them. For AI and circular economy to work together, it is necessary to educate AI developers to be more familiar with the idea of circular economy as well as making circularity practitioners and researchers more AI-savvy. Holding just half of the equation, we risk missing out on most of the intelligence. After all, no matter how smart machines can be, ultimately, it is the human intelligence – or stupidity – that determines the kind of future that we will be having.

 

Extract of “The AI Republic: Building the Nexus Between Humans and Intelligent Automation”

 

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Business

USING ANALYTICS TO CHEAT TO SECURE THE ONE RESOURCE THAT MONEY CAN’T BUY

By Avtar Dhillon, Director, Business Value Consulting, ThoughtSpot

 

“It’s not that we have little time, but more that we waste a good deal of it” – Seneca.

Time is precious. Time lost cannot be regained. There are a lot of sayings about the fleeting nature of time. Right now, time is flying. In a volatile market where enterprises might need billions in revenue just to stand still there is a pressing need to understand how to compete and build back better as we go into 2021. Take the mortgage market. Lenders lost billions of pounds when lockdown stopped the housing market.

A report conducted by the Economist Intelligence Unit found this year that while AI adoption is widely in use by most financial institutions, 86% of banks and insurance companies plan to increase AI-related investment into technology by 2025. The research was drawn from retail and investment banks and insurance companies across Europe, APAC, and the US.

One of the main reasons for this adoption comes down to time. Analytics and AI can support smarter financial industry planning, and shave time off every activity in order to beat competition and extract maximum value from the market where possible. It’s an important consideration as many financial services firms find themselves in an uncertain position post-Brexit and during the global pandemic. The future is uncertain, markets are volatile, and businesses, consumers, and the government have all been adjusting to changing circumstances. AI is held by financial sector firms as the technology with the potential to help them maintain competitiveness in this uncertainty.

The Economist study found that while there is a strong degree of confidence in the benefits of AI, the reality is that the technology is not yet largely in use: More than half of respondents say AI is not incorporated into their processes and offerings, with only 15% saying the technology is used extensively across the organisation. However, the benefits that have already emerged combined with respondents’ plans to double down on AI investment in the short-term show this technology is slated to drive a massive wave of future growth for financial services.

Avtar Dhillon

Banks and insurance companies perceive AI as critical to unlocking new growth opportunities and reducing costs. Respondents were clear AI will transform businesses in a number of ways over the next five years, including spurring new products and services (27%), opening new markets (25%), and paving the way for innovation (25%). About one-third (29%) expect between 51% and 75% of their workloads to be supported by AI technologies in five years.

In addition to driving growth, AI promises significant savings today and in the future: 37% reported that their organisation has reduced operational costs as a result of AI adoption, and 34% predict that AI will lower their cost base over the next five years. When it comes to other benefits, one-third of respondents each reported a greater use of predictive analytics (34%), increased employee capacity to handle workload volume (33%), and enhanced customer service and satisfaction (32%).

 

Investment and retail banks emerge as AI leaders

Investment banks are deploying a higher volume of new AI applications on average when compared to retail bank and insurance peers. They are also the most advanced in the implementation of training programmes: 54% have already implemented initiatives, compared with 46% in insurance and 48% in retail banks. Additionally, investment banks are most likely to use machine learning (63%) and image analysis (52%), whereas retail banks are more heavily deploying predictive analytics (71%) and virtual assistants (61%).

The report also found that banks and insurers in APAC are trendsetters when it comes to adoption, training and measurement. 61% reported that half or more of their workload is supported by AI, compared with North America and Europe (both 41%). Europe’s low usage is partly a reporting problem: Almost 10% either had no metrics to measure AI-application success, or had not been measured for long enough to report on. By way of contrast, all APAC respondents had functional reporting metrics. Notably, APAC prioritises reskilling and training initiatives, with 75% expecting an increased investment in people over the next five years to learn more AI skills and arm them with resources compared to 59% in North America and 37% in Europe.

 

The path forward: Upskilling frontline knowledge workers

Despite relatively slow adoption rates to date, the promise of AI holds clear with 86% of respondents saying they plan to increase AI-related investments into the technology over the next five years. However, greater AI adoption will ultimately be driven by how much financial services organisations invest into upskilling their workforce. This upskilling is required to get real value from democratising insights.

According to the data, the industry is at a halfway point when it comes to upskilling their employees, with 49% of respondents saying training initiatives for employees to better understand AI are currently in place. Another 42% have plans to implement such training.

With cost reduction plans working (for over a third) or forecast (another third) using AI, as the data shows, the way ahead revolves around driving new growth. AI is seen to be the new growth engine, and the key to unlocking its potential requires investing in talent. Financial services companies must lower the technology barrier for ordinary employees to capitalise on the productivity and innovation gains made possible by AI. This is the end goal for most of the financial services industry, and many others – to be able to react at the speed of thought to changing conditions, markets, and information – bringing us back to the point: Making the best use of time, because getting to understanding has not been a fast process in the history of business intelligence.

 

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