Monthly Archives: November 2015

Driverless cars will change the way we think of car ownership

Hussein Dia, Swinburne University of Technology

The transition to fully driverless cars is still several years away, but vehicle automation has already started to change the way we are thinking about transportation, and it is set to disrupt business models throughout the automotive industry.

Driverless cars are also likely to create new business opportunities and have a broad reach, touching companies and industries beyond the automotive industry and giving rise to a wide range of products and services.

The introduction of autonomous driving technology will be gradual.
Mojomotors, Author provided

New business models

We currently have Uber developing a driverless vehicle, and Google advancing its driverless car and investigating a ridesharing model.

Meanwhile, Apple is reportedly gearing up to challenge Telsa in electric cars and Silicon Valley is extending its reach into the auto industry.

These developments signal the creation of an entirely new shared economy businesses that will tap into a new market that could see smart mobility seamlessly integrated in our lives.

Consider, for example, the opportunity to provide mobility as a service using shared on-demand driverless vehicle fleets. Research by Deloitte shows that car ownership is increasingly making less sense to many people, especially in urban areas.

Individuals are finding it difficult to justify tying up capital in an under-utilised asset that stays idle for 20 to 22 hours every day. Driverless on-demand shared vehicles provide a sensible option as a second car for many people and as the trend becomes more widespread, it may also begin to challenge the first car.

Results from a recent study by the International Transport Forum that modelled the impacts of shared driverless vehicle fleets for the city of Lisbon in Portugal demonstrates the impacts. It showed that the city’s mobility needs can be delivered with only 35% of vehicles during peak hours, when using shared driverless vehicles complementing high capacity rail. Over 24 hours, the city would need only 10% of the existing cars to meet its transportation needs.

The Lisbon study also found that while the overall volume of car travel would likely increase (because the vehicles will need to re-position after they drop off passengers), the driverless vehicles could still be turned into a major positive in the fight against air pollution if they were all-electric.

It also found that a shared self-driving fleet that replaces cars and buses is also likely to remove the need for all on-street parking, freeing an area equivalent to 210 soccer fields, or almost 20% of the total kerb-to-kerb street space.

Other studies have also shown that dynamic ridesharing using driverless vehicles will increase vehicle utilisation up to eight hours per day.

Car insurance

A recent study by McKinsey on disruptive technologies suggests that up to 90% of all accidents could be prevented by driverless vehicles. So why buy insurance if automation makes accidents far less likely?

“The truth is, if it’s a safer way of driving, it’s good for society and it’s bad for our insurance business,” the US business magnate Warren Buffet said recently when asked about the impact driverless vehicles may have on his GEICO car insurance subsidiary.

“Anything that cuts accidents by 30%, 40%, 50% would be wonderful, but we won’t be holding a party at our insurance company.”

Other studies have speculated that premiums could be reduced by 75%, especially if drivers are no longer required to get coverage, and liability is shifted from drivers to manufacturers and technology companies.

Under this scenario, insurers might move away from covering private customers from risk tied to “human error” to covering manufacturers and mobility providers against technical failure.

A Rand Corporation report also predicts that drivers might end up covering themselves with health insurance instead of vehicle insurance.

Will driverless vehicles destroy the very idea of ownership?

Does all this mean car ownership is passé? In some ways, you may not own every facet of your driveless car anyway. Vehicle manufacturers are arguing that since they own the software that runs a connected vehicle, they also own the machine that runs that program.

In comments submitted to the US Copyright Office, vehicle manufacturers argue that purchasers are only licensing the product and it would be unsafe for them to modify the vehicle programming or even make a repair. The Copyright Office is currently holding a hearing on the issue. If it rules in favour of the manufacturers, it will set a precedent that can change the whole landscape of vehicle ownership.

Not everyone will be excited by this vision, and many would be sceptical and disagree that we are at the cusp of a transformation in mobility. Others still want to drive and not everyone is likely to want to rideshare on a daily basis. Many might also argue that better investment in public transport would achieve similar outcomes.

Whether you embrace or object to these scenarios, the reality is driverless vehicles are coming and they will have socio-economic impacts and other effects on our society – some good and some bad.

I see them, along with urban transport technologies, as having a role in delivering new mobility solutions as part of a holistic approach to improve road safety and promote low carbon mobility. The market will ultimately determine whether they can succeed.

Hussein Dia, Associate professor, Swinburne University of Technology

This article was originally published on The Conversation. Read the original article.

Why sticking with your manager is better for football clubs in the long-run

Simon Chadwick, Coventry University

You’re not special, you’re not special, you’re not special anymore!

So, football fans have been chanting at Chelsea manager, Jose Mourinho, who famously once referred to himself as the “special one” for his managerial skills. The mockery is not without reason.

Last season’s champions are currently languishing in the lower reaches of the Premier League table and speculation has been rife about Mourinho’s future. His truculence before the media should not be taken as an indication of a desire to quit, though. Jose has always been the master of creating a siege mentality, deliberately positioning the clubs and players he has coached as victims of great conspiracies.

But it is hard to recall a time when one of his teams has performed this badly. Chelsea already have lost six Premier League games – they lost just three during the entire 2014-15 season. Many are therefore questioning just how much longer Mourinho should be kept on as manager.

Having arguably mismanaged Mourinho’s first departure from the West London club back in 2007, Chelsea’s board of directors don’t seem to be making too many noises publicly about him. But the club’s Russian oligarch owner, Roman Abramovich, is known for being rather impatient with the managers he employs, so for many it’s a case of when – not if – Mourinho is sacked.

The question is, then: is it better for a club to sack a manager sooner, or later?

There is conflicting research about the point in a season at which a struggling manager or coach should lose their job. By sacking a manager straightaway, the argument is that there will still be time to attract a new one. After all, the replacement will also need time to settle into their new position, and turn the club’s performance around. Indeed, in Chelsea’s case, if Mourinho were to be sacked at this point it would leave the incumbent 27 games (or 81 points) to hoist the club back up the league.

But other research shows there may be a “honeymoon period” for new managers, during which results initially improve … before continuing in a downward trajectory. So Mourinho could be given a chance to turn things around into the new year and if results fail to pick up, a new manager could be brought in for the second half of the season. The club may then benefit from a new manager’s probable honeymoon period of good results.

Other commentators alternatively contend that the apparent failure of a manager is too often used by directors to mask other failings inside their clubs, such as the paucity of financial resources they provide their manager with. But having spent £66m (with net transfer spending of £32m) during the last player transfer window, Mourinho can hardly claim to have been constrained in this regard.

There is a possibility, too, that dismissing Mourinho would merely be a proxy for confronting more fundamental issues faced by the club. Reflections on his first spell in charge reveal that even back in 2007, Chelsea was grappling with damaging internal matters. If this is the case, whether it is acknowledged by the club or not, there would be little to gain by replacing him.

Benefits of stability

The alternative scenario to an imminent sacking is that Abramovich, having courted Mourinho for a second time in 2013, might be inclined to give his manager until the end of the season to change the club’s fortunes. There is some sense in this approach; after all, a world-class manager with a strong record of achievement doesn’t become a total failure in just 11 Premier League games.

Another body of research becomes applicable at this point, as it emphasises the importance of retaining a manager, at least until the end of a season. Some researchers argue that the performance benefits of managerial stability outweigh whatever advantages might come from a mid-season swap. Stability is acknowledged as being important in helping turn around a team’s fortunes, largely because it brings a degree of certainty and clarity to plans for the remaining months of a season.

The ‘special one’.

This may account for the respective current approaches of both club and manager in handling the uncertainty surrounding Mourinho’s future. As the Chelsea boss said in his Champions League pre-match press conference: “I’ll face bad results with honesty and dignity.”

Ultimately, if the Chelsea board wants to draw inspiration from the academic research then, on balance, the evidence appears to suggest that leaving Mourinho in charge is probably the best course of action – at least until the end of the season. Statistically, it is likely that keeping him in post will yield more points than replacing him with someone else. This is especially the case right now, as few high quality replacement managers are currently available for hire.

But Premier League football is an uncompromising business. Chelsea needs the financial rewards that a top-four finish generates, not least because it brings the riches of UEFA Champions League qualification. Furthermore, Mourinho and the club are constantly being scrutinised and the Portuguese is prone to making controversial statements, attention neither of them needs.

The academic research – and his reputation – may support his case, but if Mourinho wants to stay on at Chelsea, even the “special one” will have to dig deep.

Simon Chadwick, Professor of Sport Business Strategy, Coventry University

This article was originally published on The Conversation. Read the original article.

What problems will AI solve in future? An old British gameshow can help explain

Ian Miguel, University of St Andrews and Patrick Prosser, University of Glasgow

The Crystal Maze, the popular UK television show from the early 1990s, included a puzzle that is very useful for explaining one of the main conundrums in artificial intelligence. The puzzle appeared a few times in the show’s Futuristic Zone, one of four zones in which a team of six contestants sought to win “time crystals” that bought time to win prizes at the Crystal Dome at the end of the show.

Never solved in the two-minute time frame, the puzzle was based on a network of connected red circles (see clip below). On the wall was written a clue: “No consecutive letters in adjacent circles”. The letters A to H were printed on circular plates which could be fitted onto each circle.

So what is the right approach? We might start by considering which circles are hardest to label. With a little thought, you might choose the two middle circles, since they have the most connections. Now consider which letters might best be put on them: A and H are natural candidates because they each have only one neighbour (B and G, respectively). We might put them into the grid like this:

Ian Miguel

We can now do some deduction to eliminate incompatible possibilities for the other circles. For example the top-left circle is connected to both of the central circles. Since no consecutive letters can appear in connected circles, it can’t now contain B or G. Similar reasoning can be applied to the top-right, bottom-left, and bottom-right circles:

Ian Miguel

The leftmost and rightmost circles have to be treated differently, since each is only adjacent to one central circle. On the left we can rule out B, and on the right we can rule out G:

Ian Miguel

Look carefully at the remaining options and only the leftmost circle still has G as a possibility, and only the rightmost circle has B. Once we put them in place, we can remove further possibilities from the adjacent circles:

Ian Miguel

It is now time to make another guess. It seems reasonable to start with the top-left circle and try its first possibility: C. This allows us to rule out D from the adjacent circle and C from the bottom left. If we now guess E for the top-right circle, the bottom-left circle has only one possibility left, D, which leaves just F for the bottom-right circle. We have a solution:

Ian Miguel

Decisions, decisions

This puzzle is an example of a much wider class of decision-making problems that arise in our lives, such as rostering decisions in a hospital or factory, scheduling buses or trains, or designing medical experiments. To save us the aggravation of coming up with the best solutions, one of the challenges for artificial intelligence is to develop a general way of representing and reasoning about them.

One method is known as the constraint satisfaction problem. Just like our Crystal Maze puzzle, problems that fit this model involve a set of required decisions (“cover each circle with a plate”); a fixed set of possibilities (“use the plates from A to H provided”); and a set of constraints that allow only certain combinations of possibilities (“no consecutive letters in adjacent circles”). If you input the requirements for your particular problem into a piece of software known as a constraint solver, it can then try to solve it. It will do this in much the same way as we solved the puzzle: it combines guessing (we call this “search”) with deduction, ruling out possibilities that cannot be part of a solution based on the decisions made so far.

The greatest challenge for programmers in this field is that as you increase the size of the input problem, it quickly becomes much harder to find solutions. This is directly related to how the software “guesses” the answer. Although our guesses proved correct in our simple puzzle, in AI they can often lead us down blind alleys. With large problems there can be a vast number of possibilities and a similarly vast number of dead ends.

One key question is whether there is some way of reaching solutions without going down these alleys. As yet, we don’t know. This directly relates to one of the most important open questions in computer science, the P vs NP problem, for which the Clay Mathematics Institute in the US is offering Us$1m (£657,000) for a solution. It essentially asks whether every problem whose answer can be checked quickly by a computer can also be quickly solved by a computer.

Until someone solves it, the prevailing view is that it cannot. If so, our software does have to search through all the possible guesses, in which case we need to make it as efficient as possible. One important factor here is the search strategy – which decision we tell the computer to focus on next and which value we assign to it. Also very important is what we decide are the requirements for the particular problem. Mapping our puzzle to a constraint satisfaction template was straightforward, but in real life there are often many different options. Choosing the right strategy and model can be the difference between finding a quick solution and failing in any practical amount of time.

We have now reached the stage where the latest constraint-solving software can solve far more complex practical problems than, say, ten years ago. It was used to plan the scientific activities of the Philae comet lander last year, for instance. It also offers a better way of organising evacuation schedules for large-scale disasters.

Constraint solving has found most success with scheduling problems, but there are other similar AI tools that are more useful for other types of questions. We won’t go into them here, but they include the likes of propositional satisfiability, evolutionary algorithms and mathematical programming techniques. The job of specialists is to analyse a problem, identify which combination of tools will be the most successful for a particular case, and put together a bespoke piece of software. Once computers can do this analysis and identification, hopefully only a few years in the future, we will have made a huge leap forward. Meanwhile, the battle to make each of these tools as powerful as possible continues.

Ian Miguel, Professor of Computer Science, University of St Andrews and Patrick Prosser, Senior Lecturer in Computer Science, University of Glasgow

This article was originally published on The Conversation. Read the original article.