Home » Machine Economy (Page 2)

Category Archives: Machine Economy

AI not yet but Machine Learning and Big Data are rapidly evolving

Solve problems

In his book Adventures in the Screen Trade, the hugely successful screenwriter William Goldman’s opening sentence is – “Nobody knows anything.” Goldman is talking about predictions of what might and what might not succeed at the box office. He goes on to write: “Why did Universal, the mightiest studio of all, pass on Star Wars? … Because nobody, nobody — not now, not ever — knows the least goddamn thing about what is or isn’t going to work at the box office.” Prediction is hard, “Not one person in the entire motion picture field knows for a certainty what’s going to work. Every time out it’s a guess.” Of course history is often a good predictor of what might work in the future and when, but according to Goldman time and time again predictions have failed miserably in the entertainment business.

It is exactly the same with technology and Artificial Intelligence (AI), probably more than any other technology has fared the worst when it comes to predictions of when it will be available as a truly ‘thinking machine.’ Fei-Fei Li, director of the Stanford Artificial Intelligence Lab even thinks: “today’s machine-learning and AI tools won’t be enough to bring about real AI.” And Demis Hassabis founder of Google’s DeepMind (and in my opinion one of the most advanced AI developers) forecasts: “it’s many decades away for full AI.”

Researchers are however starting to make considerable advances in soft AI. Although with the exception of less than 30 corporations there is very little tangible evidence that this soft AI or Deep Learning is currently being used productively in the workplace.

Some of the companies currently selling or and/or using soft AI or Deep Learning to enhance their services; IBM’s Watson, Google Search and Google DeepMind, Microsoft Azure (and Cortana), Baidu Search led by Andrew Ng, Palantir Technology, maybe Toyota’s new AI R&D lab if it has released any product internally, within Netflix and Amazon for predictive analytics and other services, the insurer and finance company USAA, Facebook (video), General Electric, the Royal Bank of Scotland, Nvidia, Expedia and MobileEye and to some extent the AI light powered collaborative robots from Rethink Robotics.

There are numerous examples of other companies developing AI and Deep Learning products but less than a hundred early-adopter companies worldwide. Essentially soft AI and Deep Learning solutions, such as Apple’s Siri, Drive.ai, Viv, Intel’s AI solutions, Nervana Systems, Sentient Technologies, and many more are still very much in their infancy, especially when it comes to making any significant impact on business transactions and systems processes.

Machine Learning

On the other hand, Machine Learning (ML), which is a subfield of AI, which some call light AI, is starting to make inroads into organizations worldwide. There are even claims that: “Machine Learning is becoming so pervasive today that you probably use it dozens of times per day without knowing it.”

Although according to Intel: “less than 10 per cent of servers worldwide were deployed in support of machine learning last year (2015).” It is highly probable Google, Facebook, Salesforce, Microsoft and Amazon would have taken up a large percentage of that 10 percent alone.

ML technologies, such as the location awareness systems like Apple’s iBeacon software, which connects information from a user’s Apple profile to in-store systems and advertising boards, allowing for a ‘personalized’ shopping experience and tracking of (profiled) customers within physical stores. IBM’s Watson and Google DeepMind’s Machine Learning have both shown how their systems can analyze vast amounts of information (data), recognize sophisticated patterns, make significant savings on energy consumption and empower humans with new analytical capabilities.

The promise of Machine Learning is to allow computers to learn from experience and understand information through a hierarchy of concepts. Currently ML is beneficial for pattern and speech recognition and predictive analytics. It is therefore very beneficial in search, data analytics and statistics – when there is lots of data available. Deep Learning helps computers solve problems that humans solve intuitively (or automatically by memory) like recognizing spoken words or faces in images.

Neither Machine Learning nor Deep Learning should be considered as a attempt to simulate the human brain – which is one goal of AI.

Crossing the chasm – not without lots of data

If driverless vehicles can move around with decreasing problems, this is not because AI has finally arrived, it is not that we have machines that are capable of human intelligence, but it is that we have machines that are very useful in dealing with big data and are able to make decisions based on uncertainties regarding the perception and interpretation of their environment – but we are not quite there yet! Today we have systems targeted at narrow tasks and domains, but not that promised by ‘general purpose’ AI, which should be able to accomplish a wide range of tasks, including those not foreseen by the system’s designers.

Essentially there’s nothing in the very recent developments in machine learning that significantly affects our ability to model, understand and make predictions in systems where data is scarce.

Nevertheless companies are starting to take notice, investors are funding ML startups, and corporations recognize that utilizing ML technologies is a good step forward for organizations interested in gaining the benefits promised by Big Data and Cognitive Computing over the long term. Microsoft’s CEO, Satya Nadella, says the company is heavily invested in ML and he is: “very bullish about making machine learning capability available (over the next 5 years) to every developer, every application, and letting any company use these core cognitive capabilities to add intelligence into their core operations.”

The next wave – understanding information

Organizations that have lots of data know that information is always limited, incomplete and possibly noisy. ML algorithms are capable of searching the data and building a knowledge base to provide useful information – for example ML algorithms can separate spam emails from genuine emails. A machine learning algorithm is an algorithm that is able to learn from data, however the performance of machine learning algorithms depends heavily on the representation of the data they are given.

Machine Learning algorithms often work on the principle most widely known as Occam’s razor. This principle states that among competing hypotheses that explain known observations equally well one should choose the “simplest” one. In my opinion this is why we should use machines only to augment human labor and not to replace it.

Machine Learning and Big Data will greatly compliment human ingenuity – a human-machine combination of statistical analysis, critical thinking, inference, persuasion and quantitative reasoning all wrapped up in one.

“Every block of stone has a statue inside it and it is the task of the sculptor to discover it. I saw the angel in the marble and carved until I set him free.” ~ Michelangelo (1475–1564)

The key questions businesses and policy makers need to be concerned with as we enter the new era of Machine Learning and Big Data:

1) who owns the data?

2) how is it used?

3) how is it processed and stored?

Update 16th August 2016

There is a very insightful Quora answer by François CholletDeep learning researcher at Google where he confirms what I have been saying above:

“Our successes, which while significant are still very limited in scope, have fueled a narrative about AI being almost solved, a narrative according to which machines can now “understand” images or language. The reality is that we are very, very far away from that.”


Photo credit, this was a screen grab of a conference presentation, now I do not remember the presenter or conference but if I find it I will update the credit!





When machines replace jobs, the net result is normally more new jobs

Two of the current leading researchers in labor economics studying the impact of machines and automation on jobs have released a new National Bureau of Economic Research (NBER) working paper, The Race Between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment.

The authors, Daron Acemoglu and Pascual Restrepo are far from the robot-supporting equivalent of Statler and Waldorf, the Muppets who heckle from the balcony, unless you consider their heckling is about how so many have overstated the argument of robots taking all the jobs without factual support:

Similar claims have been made, but have not always come true, about previous waves of new technologies… Contrary to the increasingly widespread concerns, our model raises the possibility that rapid automation need not signal the demise of labor, but might simply be a prelude to a phase of new technologies favoring labor.

In The Race Between Machine and Man, the researchers set out to build a conceptual framework, which shows which tasks previously performed, by labor are automated, while at the same time more ‘complex versions of existing tasks’ and new jobs or positions in which labor has a comparative advantage are created.

The authors make several key observations that show as ‘low skilled workers’ are automated out of jobs, the creation of new complex tasks always increases wages, employment and the overall share of labor increases. As jobs are eroded, new jobs, or positions are created which require higher skills in the short term:

Whilst “automation always reduces the share of labor in national income and employment, and may even reduce wages. Conversely, the creation of new complex tasks always increases wages, employment and the share of labor.”

They show, through their analysis, that for each decade since 1980, employment growth has been faster in occupations with greater skill requirements

During the last 30 years, new tasks and new job titles account for a large fraction of U.S. employment growth.

In 2000, about 70% of the workers employed as computer software developers (an occupation employing one million people in the US at the time) held new job titles. Similarly, in 1990 a radiology technician and in 1980 a management analyst were new job titles.

Looking at the potential mismatch between new technologies and the skills needed the authors crucially show that these new highly skilled jobs reflect a significant number of the total employment growth over the period measured as shown in Figure 1:

From 1980 to 2007, total employment in the U.S. grew by 17.5%. About half (8.84%) of this growth is explained by the additional employment growth in occupations with new job titles.

Figure 1

Unfortunately we have known for some time that labor markets are “Pareto efficient; ” that is, no one could be made better off without making anyone worse off. Thus Acemoglu and Restrepo point to research that shows when wages are high for low-skill workers this encourage automation. This automation then leads to promotion or new jobs and higher wages for those with ‘high skills.’

Because new tasks are more complex, the creation may favor high-skill workers. The natural assumption that high-skill workers have a comparative advantage in new complex tasks receives support from the data.

The data shows that those classified as high skilled tend to have more years of schooling.

For instance, the left panel of Figure 7 shows that in each decade since 1980, occupations with more new job titles had higher skill requirements in terms of the average years of schooling among employees at the start of each decade (relative to the rest of the economy).

Figure 7

However it is not all bad news for low skilled workers the right panel of the same figure also shows a pattern of “mean reversion” whereby average years of schooling in these occupations decline in each subsequent decade, most likely, reflecting the fact that new job titles became more open to lower-skilled workers over time.

Our estimates indicate that, although occupations with more new job titles tend to hire more skilled workers initially, this pattern slowly reverts over time. Figure 7 shows that, at the time of their introduction, occupations with 10 percentage points more new job titles hire workers with 0.35 more years of schooling). But our estimates in Column 6 of Table B2 show that this initial difference in the skill requirements of workers slowly vanishes over time. 30 years after their introduction, occupations with 10 percentage points more new job titles hire workers with 0.0411 fewer years of education than the workers hired initially.

Essentially low-skill workers gain relative to capital in the medium run from the creation of new tasks.

Overall the study shows what many have said before, there is a skills gap when new technologies are introduced and those with the wherewithal to invest in learning new skills, either through extra education, on the job training, or self-learning are the ones who will be in high demand as new technologies are implemented.



New research ‘fears of technological change destroying jobs may be overstated’


Frank Levy an economist and Professor at MIT and Harvard, who work’s on technology’s impact on jobs and living standards, has written to assay the sensationalized fears of the overhyped study by Frey and Osborne. Levy indicates:

  • The General Proposition – Computers will be subsuming an increasing share of current occupations – is unassailable.
  • The Paper (Frey and Osborne study) is a set of guesses with lots of padding to increase the appearance of “scientific precision.”
  • The authors’ understanding of computer technology appears to be average for economists (= poor for computer scientists). By my personal guess, they are overestimating what current technology can do.

Researchers at the OECD analyzed the Frey and Osborne study and conducted their own research on tasks and jobs and concluded that: “automation was unlikely to destroy large numbers of jobs.”

I have also been quite critical of the Frey and Osborne study based on my understanding of technological advances, which they claim to be way more ahead than it is:

We argue that it is largely already technologically possible to automate almost any task, provided that sufficient amounts of data are gathered for pattern recognition.

With the exception of three bottlenecks, namely:

“Perception and manipulation.”

“Creative intelligence.”

“Social intelligence.”

Frey and Osborne divided the tasks involved in jobs along two dimensions: cognitive vs. manual and non-routine vs. routine. They then identified three aspects (bottlenecks) of a job making it less likely that a computer would be able to replicate the tasks of that job: First, “perception and manipulation” in unpredictable tasks such as handling emergencies, performing medical treatment, and the like. Second, “creative intelligence” such as cooking, drawing, or any other task involving creative values relying on novel combinations of inspiration; Third, “social intelligence”, or the real-time recognition of human emotion.

Race with the machines

Now a new research paper, released in July 2016, by researchers at the Centre for European Economic Research has indicated that technology has in fact had the opposite impact and is a net creator of jobs not destroyer (at least in 27 European countries – and I suspect the same is true for other regions).

The paper, Racing With or Against the Machine? Evidence from Europe by authors Terry Gregory, Anna Salomons, and Ulrich Zierahn (Gregory and Zierahn were also two of the OECD paper authors) looked at the impact of routine replacing technology on jobs and concluded:

Overall, we find that the net effect of routine-replacing technological change (RRTC ) on labor demand has been positive. In particular, our baseline estimates indicate that RRTC has increased labor demand by up to 11.6 million jobs across Europe – a non-negligible effect when compared to a total employment growth of 23 million jobs across these countries over the period considered. Importantly, this does not result from the absence of significant replacement of labor by capital. To the contrary, by performing a decomposition rooted in our theoretical model, we show that RRTC has in fact decreased labor demand by 9.6 million jobs as capital replaces labor in production. However, this has been overcompensated by product demand and spillover effects which have together increased labor demand by some 21 million jobs. As such, fears of technological change destroying jobs may be overstated: at least for European countries over the period considered, we can conclude that labor has been racing with rather than against the machine in spite of these substitution effects.

My research of companies using robots has also categorically shown, through factual evidence, that those companies have created significantly more jobs than have been lost due to technological change. Similarly a detailed analysis prepared for the European Commission Director General of Communications Networks, Content & Technology by Fraunhofer about the impact of robotic systems on employment in the EU found that:

European manufacturing companies do not generally substitute human workforce capital by capital investments in robot technology. On the contrary, it seems that the robots’ positive effects on productivity and total sales are a leverage to stimulate employment growth.

So if robots are not job killers what is the real problem?

We need to fill the skills gap

I have argued before that we have a skills problem. Jobs all over the world are not being filled because of lack of skilled personnel to fill them.

New and emerging technologies both excite and worry. Robotics and Artificial Intelligence (AI) is certainly a minefield for both exuberance and fears.

By definition, there is a knowledge and skills gap during the emerging stages of any new technology, Robotics and AI is no exception: researchers and engineers are still learning about these technologies and their applications. But, in the meantime, hope, fears and hype naturally and irresistibly fill this vacuum of information.

Depending on whom you ask Robots and AI is predicted to help solve the world’s problems. Or by building this devil, these technologies may scorch the earth and fulfill a prophecy of Armageddon.

On the other side, especially with respect to AI, what it will most likely do – if and only if adopted by major corporations and governments — is foster technological and institutional betterment at a frenetic pace through improved health care, solving climate problems, helping those with sight problems, helping to get much needed aid spread more equitably.

We need education and training fitted to a different labour market, with more focus on creativity, flexibility and social skills. We need more Moonshots from Governments and Industry as so well described by Mariana Mazzucato in her book the Entrepreneurial State: Debunking Public vs. Private Sector.

Machines are there to augment human intelligence and ingenuity, to improve our environment and workplace, we need to stop fearing the machines and learn how to better integrate them into our processes, destroy the fears and improve productivity. We are not going to stop technological progress, if we embrace it we are better prepared to gain from it.

Goldman Sachs summary on Cobots

Goldman Sachs cobots


Goldman Sachs (“GS”) has released a series of research reports in 2016 centered on The Factory of the Future.

The series which they call ‘Profiles in Innovation’ examines six technologies GS believe is driving transition, from “Cobots” to 3D printing to Virtual and Augmented Reality to the Internet of Things, and how these technologies could yield more than US$500 billion of cost savings.

As part of the GS team’s investigations they hosted a Factory of the Future field-trip for investors at Automatica trade fair in Munich, Germany on June 25, 2016. They subsequently provided a synopsis of their key observations.

Here are the top takeaways from GS’s field trip to Automatica related to Robots.

Universal Robots (“UR”)

  • Universal Robots’ cobots have a payback of 6 months and overall installation costs at <2x cost of robots vs. >3x for traditional robots. Cheapest UR cobot costs just €20k.
  • Universal Robots believes its sales network, brand and open-source strategy will be important to lock-in and outgrow the cobot market.
  • Amidst its own impressive growth, Universal Robots is preparing for tougher competition.

Universal Robots, Teradyne’s market leading collaborative robots business, hosted a booth tour. Key takeaways were:

  1. With the cobot market growing >50% pa in recent years, Teradyne (owner of UR) is targeting $90 million to $100 million in revenues for Universal Robots for 2016. UR believes this fast growth is unlikely to hit capacity constraints as its current Denmark-based manufacturing set-up can generate $500 million in revenues without the need for significant factory cap expenditure.
  2. The customer base for Universal Robots consists largely of SME enterprises in a wide range of end markets. As a result, its method-to-market and ease-of-use is key to achieving rapid organic growth. It uses distributors (which pick up servicing margin in return for broad dissemination) and a user-friendly set-up, eliminating the need for third party engineers to program the robot.
  3. Universal Robots believes that its technology is 2-3 years ahead of competitors (15 other booths at the fair were using UR cobots), however it is aware that the competition is increasing significantly. Leveraging Teradyne’s balance sheet they believe acting quickly and the use of their open-source platform (meaning that a wide range of components are easy to develop, described as an “App store” approach) is key to dominating this quickly evolving market.


  • Cobot competition is picking up as Yaskawa entered the race and Fiat Chrysler’s Comau are pioneering solutions to concerns about speed.
  • Yaskawa demonstrated five of its new product launches, underpinning our growth expectations and mix improvement as it increases appeal in general industry.

Yaskawa hosted a booth tour and interview with its EU operations management. The company exhibited several new products:

  • 10 kg payload collaborative robots
  • 7-axis robots with the newest spot welding gun and smaller, low pay-load 
robots ideal for general industry.
  • Motologix software (bridging machine communication between controllers and PLCs (programmable logic controller) – based on VIPA (acquired German company PLC technology).

Goldman Sachs, who said they came away with a great deal of confidence in Yaskawa’s product mix, also offered the following key takeaways:

  1. Looking at collaborative robots specifically, GS believe the company has strong positioning as one of the ”Big Four” robotics company. They believe pricing is reasonable at €38,000 for 10kg weight handling, with sensors implemented in all axis and easy teaching system. Given that many start-up companies were introducing cobots with, in the GS teams opinion, inferior quality and yet similar pricing (€20-40,000 per unit), GS felt Yaskawa is well positioned to capture the growth of the cobot market.
  2. Yaskawa sold 25,000 robots in 2015; which GS estimate that Yaskawa has circa 10% market share (note these will be mainly premium robots), bringing Yaskawa’s total installed base to 350k.

Other general observations by the Goldman Sachs team

  • Despite the absence of a major global robotics player, the US (where robotics is growing double digit) is still at the forefront in automation, by developing the embedded technologies required.
  • Beware of the buzzwords: Most notably, AI and cloud robotics. Association for Advancing Automation thinks it might take decades to get commercializable AI products.
  • Machine vision is a >$2 billion market, despite in a current downturn, according to the Association for Advancing Automation.
  • Flexibility and efficiency are crucial in leading autos factories, as BMW produces a car in 44 hours with no two likely to be the same each day.
  • The average age of workers in BMW’s Welt factory is rising (43 vs. 40 a few years ago) as new technologies, such as exoskeletons, are increasing the longevity of employees.

Check out Goldman Sachs briefings and video for additional information.

Robots increasing wine production, while reducing environmental impact

Freixenet robots.png


Founded in Spain in 1861 in Penedès, the main district of Catalonia, Freixenet S.A. currently owns 18 wineries across three continents and is one of the best-known Spanish wine brands. The 155 years old family owned business has annual sales exceeding Euro 500 million (US$ 560 million) and produces over 200 million bottles of sparkling wine each year.

The sparkling wine is known as “cava” due to the fact that much of the production fermentation process is in a network of several miles of underground caves or cellars. To be branded cava, sparkling wine must be produced in the ‘champenoise traditional method’, in the past cava was referred to as “Spanish champagne”, however this branding is no longer permitted under European Union law. Nevertheless the method of production for cava and Champagne are pretty much the same in which wine is fermented twice and sugar added to make it bubbly.

Sparkling wine is currently the key growth area in the beers, wine and spirits category. This growth has caused some challenges for Freixenet to increase production capacity to the same degree as an increase in the success of the brand and its products. The challenges are compounded by the traditional methods of production which require that processes are maintained, in fact according to Josep Palau, Head of Production at Freixenet:

What has not changed at all is our traditional elaboration process, which still includes each and every one of the stages as they were undertaken 50 years ago. We collect the grapes, make the base wines, bottle them, ferment them, then the crianza process begins, disgorging, etc. But what we have done continuously is make these stages more technical and automated in order to adapt ourselves to an increase in demand.

Those changes in production also depend on the particular cava being produced; the process is either done by hand (for the very top cuvees), or increasingly by automation. For example the company now uses pneumatic presses with a soft membrane that creates a pressure similar to traditional foot treading for pressing the grapes.

Once the grapes are pressed the ‘must’ from which the base wines are made is mixed in large vats by adding sugar, yeast and clarifiers, this then undergoes a bottling process and then the wines are taken to the cellars for fermentation. The fermentation involves the use of computerized automation that slowly rotate the bottles to help the build up of the carbon dioxide gas needed for cava’s characteristic bubbles. Depending on the product, this may range from a minimum of nine months to three years or more in higher quality wines.

Of Freixenet’s 1700 employees worldwide approximately 350 are employed at their main production facility. According to Josep Palau a large number of employees are involved in heavy manual tasks of moving the bottles around.

Once the base wines are bottled, the bottles have to be stored in cellars and this requires a great deal of internal logistics.

The cellar process, whether it is positioning the bottles or retrieving them a year later for the clarifying process before disgorging, involves a lot of internal movement and labor.

To help overcome many of the handling, maneuvering and bottling problems Freixenet have installed 36 industrial robots from Fanuc. With the help of Fanuc’s robots production capability has increased substantially. Josep Palau says:

Now an operator can move 500 bottles with each action rather than the two bottles before. The disadvantage before was that, as well as continuing to need somebody to intervene manually, the process also took up a lot of space in our cellars.

The next major innovation was automating the stacking process, or placing the bottles in the cellars, which had previously been done manually until Freixenet’s technicians and a local engineer came up with and implemented a robotic system that allowed the job to be done more efficiently. Mr. Palau believes this automation was the most significant milestone in improving productivity and reducing waste:

This was probably one of the most important innovations that was introduced. Later, and in the aim of being able to manage a great number of bottles, a new bottling process was created, which was almost completely automated and was fully robotized during the end stage. The bottles leave the production line via an automated transport system and arrive directly to the cellars, where an automatic robot system positions them in place for the crianza stage.

By automating this process, work was greatly simplified and our ability to handle this removal step increased enormously, thereby allowing us to handle growth.

In addition to increasing productivity by more than 32 per cent since the introduction of the robots and securing jobs, Freixenet have also discovered environmental benefits from the new technology for bottling and handling. The automation has resulted in a reduction of 25 per cent of the organic pollution load, chemical oxygen demand (COD) of wastewater per unit produced between 2012-2014, and glass waste has been reduced by 7 per cent.

In Spain, one of the key dates on the calendar in the run-up to Christmas is the first broadcast of the Freixenet TV advert. A tradition established in 1978, which has been graced through the years by celebrities such as Demi Moore, Pierce Brosnan, Penélope Cruz, Kim Basinger, Sharon Stone, Antonio Banderas, Paul Newman, Josep Carreras, Plácido Domingo, and many more. The celebrities of the 2012 campaign were two of Freixenet’s production Fanuc robots saluting with 2 glasses of cava. Cheers!






The future is looking up for Insurance companies and drones



The existence of new drone regulations hasn’t dampened the appetite of prospective drone users for commercial purposes. There’s a ground swell of commercial users looking to get permission for drone use in areas as diverse as retail deliveries, agriculture crop spraying, real-estate sales, commercial photography and filmmaking, search and rescue operations, and oil spill monitoring and an abundance of other sectors.

Governments’ approval is seen as a first step in unleashing a potentially multibillion dollar industry that so far has been largely limited to military and law enforcement applications and more recently monitoring of pipelines along Alaska’s northern shore and energy lines of the National Grid in the UK.

As regulations are clarified and ratified one industry that has seen early adoption of drones is the Insurance sector. In a recent report[1] by PwC, the global audit and consulting firm estimates:

The addressable market of drone powered solutions in the insurance industry at US$ 6.8 billion.

There are three areas where drone operations can enhance an insurer’s procedures: risk monitoring, risk assessment and claims management

Claims management

After a natural catastrophe, a drone could reach a remote scene much faster than a claims adjuster.

The largest insurance loss event globally in 2015, of both natural and man-made disasters, was the two explosions at the Port of Tianjin in China, which triggered property claims of between US$ 2.5 to US$ 3.5 billion according to reinsurance company Swiss Re. This was also the largest man-made insured loss event in Asia ever recorded.

The Tianjin explosions have presented insurers with a number of challenges, not least lack of access to the affected area to assess the full extent of damage and resulting insurance claims.

According to a report[2] from insurer Swiss Re:

Drone and satellite imagery have helped loss assessment (at the Port of Tianjin). Drones were sent in to take pictures of the disaster site immediately after the explosions.

These images were compared with satellite images of the site taken prior to the event.

The comparison provided a view of the extent of destruction, and also of the high number of vehicles and containers on the site at the time of the explosion. Initial loss assessments have been based on this information.

This would not have been possible without drones because of the 3 kilometers (1.86 miles) radius exclusion zone enforced at the site. The alternative would have been to wait until the exclusion zone was relaxed and use manned aircraft to take pictures after the event from high altitude, which would have been more expensive and may not have produced the same quality images.

Drones have the advantage of being small, low-cost and able to closely survey and photograph large areas more efficiently. Damaged areas such as Tianjin may not be visible by satellites and manned aircraft, for example due to dust cover, or may be inaccessible for first-hand human inspection due to contamination or transport outages after a disaster event.

Another example of where drones are now being deployed to areas unreachable by claims adjusters is in a flood zone. In December 2015, drones were used to take pictures over Cumbria in the UK after large areas were flooded due to Storm Desmond.[3] The images allowed for better response planning, and loss adjusters used them to identify the worst- affected areas and properties for which claims were reported, which in turn facilitated initial claims reserving.

Significant cost savings

AXA Group, the world’s largest insurer with revenues approaching US$ 100 billion and a recently released strategy to become a leader in digital and technological insurance is carrying out trials of drones in France and Belgium. The company says[4]:

Drones fly over inaccessible damaged areas to gather images or videos, which are immediately sent to remote claims adjusters so they can update clients on the loss, trigger communication and potentially advance payments to clients. Using drones can therefore increase trust and transparency and improve the customer experience.

Besides the speed of deploying resources and payments to those insured, the cost savings to insurers could be significant. No longer must underwriters travel in person to inspect the exterior of a building or property. Details of a risk could be validated without incurring travel costs or costs to make in-person inspections.

After a claim is filed, an adjuster could dispatch a drone to investigate the claim. Instead of climbing a ladder to inspect an icy patch of a damaged roof, a claims adjuster could dispatch a drone to conduct the inspection.

Drones can also survey objects from the side rather than just from above, and can facilitate 3D reconstruction of an environment using stereoscopic cameras. These are valuable inputs for improved damage assessment.

Drones could certainly save insurance carriers the costs associated with claims’ adjusters’ worker’s compensation claims.

Drones provide underwriters and claims personnel with a safe, cost-effective alternative to physical inspections.

There are many obstacles still to overcome, privacy issues, data protection, nuisance, physical or bodily harm. These obstacles present a new opportunity to insurers – as individuals and companies obtain Certificate of Authority to fly drones, to become drone pilots, these individuals and companies will also require insurance coverage for their drone activities. A study[5] commissioned by the European Commission found that drone operations do carry the potential to generate liability claims requiring lengthy and complex legal proceedings.

While insurance company use of, and indeed insurance coverage for, commercial drones is “up in the air,” there’s no question that the drone market is a key growth area.




[1] PwC Global, Clarity from Above May 2016 (https://www.pwc.pl/pl/pdf/clarity-from-above-pwc.pdf) Last accessed July 5th, 2016

[2] Swiss Re, Sigma Number 1/2016 “Natural catastrophes and man-made disasters in 2015”

[3] “Drones will transform loss adjusting”, Insurance Day, January 2nd, 2016 
(https://www.insuranceday.com/news_analysis/special_reports/drones-will-transform-loss-adjusting.htm) last accessed July 5th, 2016

[4]Axa Drones Start-in 2016 (https://www.axa.com/en/newsroom/news/start-in-2016. Last accessed July 5th, 2016

[5] Steer Davies Gleave for European Commission, 2014. Study on the Third-Party Liability and Insurance Requirements of Remotely Piloted Aircraft Systems (RPAS) (https://www.eurocontrol.int/sites/default/files/ec_rpas_final_report_nov14_steer_davies.pdf) Last accessed July 5th, 2016

Picture credit Brian Moore Draws Creative Commons




Whitehouse Chairman of Economic Advisors – Why We Need More Artificial Intelligence

Society is caught between blind faith in technology and resistance to progress, between technological possibilities and fears that it has a negative impact.

Increasingly Artificial Intelligence, the latest buzzword for everything software related, is stirring up much of the fears.

In an interesting paper: Is This Time Different? The Opportunities and Challenges of Artificial Intelligence, Jason Furman, Chairman of President Obama’s Council of Economic Advisers sets out his belief that we need more artificial intelligence but must find a way to prevent the inequality it will inevitably cause. Despite the labor market challenges we may need to navigate, Furman’s bigger worry is that we will not invest enough in AI.

He is more pragmatic than many economists and researchers who have written ‘popular’ books on the subject but calls for more innovation if we are truly to reap the benefits AI and Robotics will bring:

We have had substantial innovation in robotics, AI, and other areas in the last decade. But we will need a much faster pace of innovation in these areas to really move the dial on productivity growth going forward. I do not share Robert Gordon’s (2016) confidently pessimistic predictions or Erik Brynjolfsson and Andrew Mcafee’s (2014) confidently optimistic ones because past productivity growth has been so difficult to predict.

Technology, in other words, is not destiny but it has a price

My worry is not that this time could be different when it comes to AI, but that this time could be the same as what we have experienced over the past several decades. The traditional argument that we do not need to worry about the robots taking our jobs still leaves us with the worry that the only reason we will still have our jobs is because we are willing to do them for lower wages.

Replacing the Current Safety Net with a Universal Basic Income Could Be Counterproductive

Furman says that AI does not create a call for a Universal Basic Income and that the claims for implementing UBI and cancelling other social welfare programs have been greatly overstated:

AI does not call for a completely new paradigm for economic policy—for example, as advocated by proponents of replacing the existing social safety net with a universal basic income (UBI) —but instead reinforces many of the steps we should already be taking to make sure that growth is shared more broadly.

Replacing part or all of that system with a universal cash grant, which would go to all citizens regardless of income, would mean that relatively less of the system was targeted towards those at the bottom—increasing, not decreasing, income inequality.

Instead our goal should be first and foremost to foster the skills, training, job search assistance, and other labor market institutions to make sure people can get into jobs, which would much more directly address the employment issues raised by AI than would UBI.

Past Innovations Have Sometimes Increased Inequality—and the Indications Suggest AI Could Be More of the Same

Relying on the questionable study by Frey and Osborne, Furman says that work by the Council of Economic Advisers, ranked the occupations by wages and found that, according to the Frey and Osbourne analysis, 83 percent of jobs making less than $20 per hour would come under pressure from automation, as compared to 31 percent of jobs making between $20 and $40 per hour and 4 percent of jobs making above $40 per hour (see Figure 1 below).


AI has not had a large impact on employment, at least not yet

Furman says the issue is not that automation will render the vast majority of the population unemployable. Instead, it is that workers will either lack the skills or the ability to successfully match with the good, high paying jobs created by automation.

The concern is not that robots will take human jobs and render humans unemployable. The traditional economic arguments against that are borne out by centuries of experience. Instead, the concern is that the process of turnover, in which workers displaced by technology find new jobs as technology gives rise to new consumer demands and thus new jobs, could lead to sustained periods of time with a large fraction of people not working.

AI has the potential—just like other innovations we have seen in past decades—to contribute to further erosion in both the labor force participation rate and the employment rate. This does not mean that we will necessarily see a dramatically large share of jobs replaced by robots, but even continuing on the past trend of a nearly 0.2-percentage-point annual decline in the labor force participation rate for prime-age men would pose substantial problems for millions of people and for the economy as a whole.

Investment in AI

Mentioning the fact that AI has not had a significant macroeconomic impact yet, Furman indicates that the private sector will be the main engine of progress on AI. Citing references that in 2015 the private sector invested US$ 2.4 billion on AI, as compared to the approximately US$ 200 million invested by the National Science Foundation (NSF).[1]

He says the government’s role should include policies that support research, foster the AI workforce, promote competition, safeguard consumer privacy, and enhance cybersecurity

AI does not call for a completely new paradigm for economic policy

AI is one of many areas of innovation in the U.S. economy right now. At least to date, AI has not had a large impact on the aggregate performance of the macroeconomy or the labor market. But it will likely become more important in the years to come, bringing substantial opportunities— and our first impulse should be to embrace it fully.

He indicates that his biggest worry about AI is that we may not get all the breakthroughs we think we can, and that we need to do more to make sure we can continue to make groundbreaking discoveries that will raise productivity growth, improving the lives of people throughout the world.

However, it is also undeniable that like technological innovations in the past, AI will bring challenges in areas like inequality and employment. As I have tried to make clear throughout my remarks, I do not believe that exogenous technological developments solely determine the future of growth, inequality, or employment. Public policy—including public policies to help workers displaced by technology find new and better jobs and a safety net that is responsive to need and ensures opportunity —has a role to play in ensuring that we are able to fully reap the benefits of AI while also minimizing its potentially disruptive effects on the economy and society. And in the process, such policies could also contribute to increased productivity growth—including advances in AI itself.

What are those policies? Truman indicates we need to develop more “human learning and skills,” increase investments in research and development, this includes Government investment and also “expand and simplify the Research and Experimentation tax credit,” “increase the number of visas—which is currently capped by legislation—to allow more high-skilled workers to come into the country.” “Consolidate existing funding initiatives, help retrain workers in skills for which employers are looking,” and more focused initiatives such as the “DARPA Cyber Grand Challenge.”

The bottom line is that AI managed well, with innovate government support, could offer significant benefits to humanity, but those benefits, including earning capacity, can only be achieved if governments and corporations help people up-skill.

[1] For private funding see https://www.cbinsights.com/blog/artificial-intelligence-funding-trends/#funding. For public funding see http://www.nsf.gov/about/budget/fy2017/pdf/18_fy2017.pdf. According to the NSF, in 2015 there was $194.58 million in funding for the NSF Directorate for Computer and Information Science and Engineering’s Division of Information and Intelligent Systems (IIS), much of which is invested in research on AI. These figures do not include investment by other agencies, including Department of Defense.