Scrapping Building Schools for the Future hurt – but it forced Liverpool to rethink its finances

Light breaks through over Liverpool. Image: Getty.

The Labour mayor of Liverpool on life after Building Schools for the Future.

Eight years ago, I picked up the pieces of our bid to have 24 schools in Liverpool rebuilt or renovated as part of the government’s Building Schools for the Future (BSF) programme.

As one of its first acts, the coalition government had scrapped the scheme. The shutters at the Department of Education abruptly came down. Civil servants we had been dealing with stopped returning our calls.

Austerity had already become Whitehall’s official religion and councils simply had to get with the programme. Cuts were coming.

Michael Gove, the then education secretary, appeared on the television and at the dispatch box making light of what, to him, was juicy £55bn slice of departmental spending he could offer up to the Treasury.

The trouble is it came at the expense of the children of Liverpool – and those in dozens of other boroughs – who were left in old, dilapidated and, in many cases, unsafe buildings that had long outlived their purpose. No-one was listening.

Left high and dry, our response was to take matters into our own hands.

We invited our various education partners to sit around the table and thrash out an alternative programme, utilising whatever council funding we could find – and all other government cash we could beg and borrow – to generate our own, localised version of BSF.

The Liverpool Schools Investment Programme was born and over the past decade, £180m has been invested in rebuilding or substantially repairing 24 schools across the city. Around 18,000 pupils in the city are now benefitting from state-of the art classroom facilities, helping with the task of ensuring our children get the best state education possible. For me, it stands as one of my proudest achievements.

However, the broader point is that we have seen public spending delegitimised over the past decade as cuts have hollowed out public services and everything from the benefits system, to the armed forces have felt the effects.


We were originally told ‘the big society’ would fill the gap. Of course, all the jumble sales in the world won’t replace the £444m taken out of our revenue support grant (RSG).

Next year’s spending review – on the back of anaemic growth and the potential shock effects of Brexit – means the situation will get worse, not better. (That’s not even mentioning the total lack of clarity about what happens when the RSG is abolished in 2020.)

There is no reprieve for local authorities, despite Tory-controlled Northamptonshire County Council actually going bust. That’s before we get onto the yawning national financial crisis in adult social care and the £2bn deficit that has opened up in children’s services.

Still, we cannot sit on our hands waiting to be rescued by a friendlier climate in Westminster and have to help ourselves. So that’s what we’re doing.

Our ‘invest to earn’ model sees us relentlessly sweat our assets in order to generate new revenue streams.

Using our capital borrowing powers, we are planning to help Everton football club build a new fit-for-purpose stadium, which will form the centrepiece of a much larger regeneration of 125-acres of our dilapidated north docks area, which have lain dormant since the 1980s. The revenue we will receive, if the deal is agreed – around £7m a year for 25 years – will be put straight back into frontline services.

A new £200m investment programme – again paid for by a mixture of savings and borrowing – will help us deliver a massive step-change in the quality of our roads network.

We have also launched a new municipal housing company, called Foundations, in order to rebalance the housing market in Liverpool. More than two-thirds of the properties in the city are in council tax Band A. This means that, for every one per cent of council tax, we raise just £1.6m. We need a better housing mix across the city to improve the sustainability of our core finances.

Our plans – although ambitious – are also prudent, with auditors from the Local Government Association recently reporting that we have a prudent level of debt and strong internal procedures for managing our finances.

Economic efficiency, then, to deliver social justice. Frankly, we have little choice but to be bold and ambitious in finding practical solutions to the problems that an austerity-led agenda has left us with, as we seek to protect the vulnerable and discharge our broader responsibilities.

For me, though, this journey began with a single, thoughtless swing of the axe from Michael Gove back in 2010.

Joe Anderson is Labour mayor of Liverpool.

 
 
 
 

Just like teenagers, self-driving cars need practice to really learn to drive

A self-driving car, of unknown level of education. Image: Grendelkhan/Flickr/creative commons.

What do self-driving cars and teenage drivers have in common?

Experience. Or, more accurately, a lack of experience.

Teenage drivers – novice drivers of any age, actually – begin with little knowledge of how to actually operate a car’s controls, and how to handle various quirks of the rules of the road. In North America, their first step in learning typically consists of fundamental instruction conveyed by a teacher. With classroom education, novice drivers are, in effect, programmed with knowledge of traffic laws and other basics. They then learn to operate a motor vehicle by applying that programming and progressively encountering a vast range of possibilities on actual roadways. Along the way, feedback they receive – from others in the vehicle as well as the actual experience of driving – helps them determine how best to react and function safely.

The same is true for autonomous vehicles. They are first programmed with basic knowledge. Red means stop; green means go, and so on. Then, through a form of artificial intelligence known as machine learning, self-driving autos draw from both accumulated experiences and continual feedback to detect patterns, adapt to circumstances, make decisions and improve performance.

For both humans and machines, more driving will ideally lead to better driving. And in each case, establishing mastery takes a long time. Especially as each learns to address the unique situations that are hard to anticipate without experience – a falling tree, a flash flood, a ball bouncing into the street, or some other sudden event. Testing, in both controlled and actual environments, is critical to building know-how. The more miles that driverless cars travel, the more quickly their safety improves. And improved safety performance will influence public acceptance of self-driving car deployment – an area in which I specialise.

Starting with basic skills

Experience, of course, must be built upon a foundation of rudimentary abilities – starting with vision. Meeting that essential requirement is straightforward for most humans, even those who may require the aid of glasses or contact lenses. For driverless cars, however, the ability to see is an immensely complex process involving multiple sensors and other technological elements:

  • radar, which uses radio waves to measure distances between the car and obstacles around it;
  • LIDAR, which uses laser sensors to build a 360-degree image of the car’s surroundings;
  • cameras, to detect people, lights, signs and other objects;
  • satellites, to enable GPS, global positioning systems that can pinpoint locations;
  • digital maps, which help to determine and modify routes the car will take;
  • a computer, which processes all the information, recognising objects, analysing the driving situation and determining actions based on what the car sees.

How a driverless car ‘sees’ the road.

All of these elements work together to help the car know where it is at all times, and where everything else is in relation to it. Despite the precision of these systems, however, they’re not perfect. The computer can know which pictures and sensory inputs deserve its attention, and how to correctly respond, but experience only comes from traveling a lot of miles.

The learning that is occurring by autonomous cars currently being tested on public roads feeds back into central systems that make all of a company’s cars better drivers. But even adding up all the on-road miles currently being driven by all autonomous vehicles in the U.S. doesn’t get close to the number of miles driven by humans every single day.

Dangerous after dark

Seeing at night is more challenging than during the daytime – for self-driving cars as well as for human drivers. Contrast is reduced in dark conditions, and objects – whether animate or inanimate – are more difficult to distinguish from their surroundings. In that regard, a human’s eyes and a driverless car’s cameras suffer the same impairment – unlike radar and LIDAR, which don’t need sunlight, streetlights or other lighting.

This was a factor in March in Arizona, when a pedestrian pushing her bicycle across the street at night was struck and killed by a self-driving Uber vehicle. Emergency braking, disabled at the time of the crash, was one issue. The car’s sensors were another issue, having identified the pedestrian as a vehicle first, and then as a bicycle. That’s an important distinction, because a self-driving car’s judgments and actions rely upon accurate identifications. For instance, it would expect another vehicle to move more quickly out of its path than a person walking.


Try and try again

To become better drivers, self-driving cars need not only more and better technological tools, but also something far more fundamental: practice. Just like human drivers, robot drivers won’t get better at dealing with darkness, fog and slippery road conditions without experience.

Testing on controlled roads is a first step to broad deployment of driverless vehicles on public streets. The Texas Automated Vehicle Proving Grounds Partnership, involving the Texas A&M Transportation Institute, University of Texas at Austin, and Southwest Research Institute in San Antonio, Texas, operates a group of closed-course test sites.

Self-driving cars also need to experience real-world conditions, so the Partnership includes seven urban regions in Texas where equipment can be tested on public roads. And, in a separate venture in July, self-driving startup Drive.ai began testing its own vehicles on limited routes in Frisco, north of Dallas.

These testing efforts are essential to ensuring that self-driving technologies are as foolproof as possible before their widespread introduction on public roadways. In other words, the technology needs time to learn. Think of it as driver education for driverless cars.

People learn by doing, and they learn best by doing repeatedly. Whether the pursuit involves a musical instrument, an athletic activity or operating a motor vehicle, individuals build proficiency through practice.

The ConversationSelf-driving cars, as researchers are finding, are no different from teens who need to build up experience before becoming reliably safe drivers. But at least the cars won’t have to learn every single thing for themselves – instead, they’ll talk to each other and share a pool of experience.

Johanna Zmud, Senior Research Scientist, Texas A&M Transportation Institute, Texas A&M University .

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