Send
Print
The head of Google, Larry Page, representing Sidewalk Labs, was not too specific:
- The aim of the project is the development of technologies at the intersection of physical and digital worlds, with a focus on improving the lives of citizens, businesses and governments.
It is clear that he is talking about a smart city and all that is connected with it - from urban mobile applications to robot janitors. But Dan Doctoroff, if you study his performances, is not only passionate about the obvious ideas at the intersection of urbanism and technology. His views on the digital future of cities are much more radical.
The main task, which, according to Doctoroff, still to be achieved even in the most modern cities, is the rapid collection of street information.
Surveillance cameras are installed throughout cities, specialist services ride round them on a regular basis. But this is not enough. According to statistics from the New York Urban Institute, analyzed data from New York, London, Melbourne, Berlin and Rio de Janeiro, the existing urban operational analyst covers only two layers of information, without fixing a lot of little things, random combination that often leads to first level problems. Modern metropolis watch pipe breaks, traffic accidents, damage to motor and power grid infrastructure in near real-time. But they still have to learn about a delay fault in the related and supporting systems.
For example, the February accident in the New York subway, when a crowded train collided at the intersection of train tracks with a sporty SUV, could not happen if the city service fixed the broken grate in the move in time (during the day), limiting the danger of leaving the side with poor visibility. And there’s more than a hundred of such exemplars in the study.
Cars are going to gather detailed information for city analysts soon, believes Doctoroff. It will not be videos – the system simply cannot store and process so much information from the millions of cars. The developers of the task instantly convert visual information into the program signal: the camera will send set of impersonal codes to the data center, and on this basis, the system is going to understand which parts in the environment and infrastructure of the metropolis were changed.
Google X Lab, which is engaged in the systems, including recognition of images, will help to turn "heavy" picture to the "light" code. And according to the developers, taxi services and public transportation management services are the first to equip their cars with microcams.
Another potential application of artificial intelligence in the cities - urban planning system. Doctoroff believes that the long-term experience in the development of master plans and already accumulated statistics allow you to create algorithms identifying and defining the trends of development of cities more accurately than architects.
Professionals already use complex programs and systems to handle large data now, but technology companies have the task of another scale: the urban environment should be extremely transparent - at the level of all available interface to provide answers to any questions related to the growth of the city to all participants interested in the process (investors, developers, builders, owners of commercial real estate, cultural projects, transport companies etc.).
- The city does not know and does not see himself, but we almost did not see the city as well - says Doctoroff.
- Today, even the most advanced systems take a few years to decide on development of the territory, since they require a thorough analysis of a long and full compliance with the general plan. The result is a radical-synchronization: the city is naturally turned to one side, the investor looks to the other, because he still has the information a year old. Business and government are losing out on billions of-synchronization. Most of the unsuccessful development projects are partially or fully explained by it. As no research institute will not be able to expedite the process, we need technological solutions.
At first glance, the idealistic concept has long been discussed in a professional environment. At its core - the desire to abandon the conservative format of the general plan, which is set in the years ahead and, accordingly, cannot take into account the numerous changes in the economy, society and technology. The master plan is adaptive in the case of the "city of big data", its strategic objectives do not change, but specific directions are formed not by hands but the computer. Thus, the general plan, Google believes, becomes a joint product of artificial intelligence and collective initiatives.
On a practical level, this may look like a public portal with a dynamic map, which shows all the processes that occur in the urban environment, space and territory in real time. Actors send project proposals, the system considers and processes them, making up the corrected long-term plans based on the incoming information.
Perhaps now, only Google is able to implement both the plan and the software system based on artificial intelligence, as well as the public interface.
It is believed that the project of unmanned vehicles, which Google develops, is aimed primarily at private mass market. In fact, the company never promised that it’s going to begin with the individual cars, and initially counted on the evolutionary transition from the general to the particular.
One of Sidewalk Labs’s fields, apparently, will be the development of public transport unmanned system. Google cars’ quality control is already very high, but it is too early to leave the pilot zones. For example, the Google cars cannot move in the pouring rain and the snow-covered areas. Identification of the area takes place after comparing pre-captured images with the results of visualization of the surrounding landscape taken by car scanning system, that is, in a normal situation, the system can tell apart an usual pedestrian from a telegraph pole, but it’s powerless in the conditions of bad weather. In addition, the cars are unable to recognize the temporary traffic signals, they cannot distinguish pedestrians from police or crumpled paper from the stone, and not least, do not know how to park.
Google plans to correct these deficiencies only in 2020, but Doctoroff believes that the current level of development is ideal for transport systems with clear limitations: the predetermined route, prescribed cycle, often physically delimited field of motion, etc.
The company has the technology, but judging by how short-spoken the company is, Sidewalk Labs’ main task in the near future will not be as much to market specific solutions as their business case. Doctoroff believes that unmanned transportation are ultimately more profitable even with the traditional costs of production of innovative, conservative citizens and other costs. Yet, it has to be proved.
source: forbes.com
- The aim of the project is the development of technologies at the intersection of physical and digital worlds, with a focus on improving the lives of citizens, businesses and governments.
It is clear that he is talking about a smart city and all that is connected with it - from urban mobile applications to robot janitors. But Dan Doctoroff, if you study his performances, is not only passionate about the obvious ideas at the intersection of urbanism and technology. His views on the digital future of cities are much more radical.
The main task, which, according to Doctoroff, still to be achieved even in the most modern cities, is the rapid collection of street information.
Surveillance cameras are installed throughout cities, specialist services ride round them on a regular basis. But this is not enough. According to statistics from the New York Urban Institute, analyzed data from New York, London, Melbourne, Berlin and Rio de Janeiro, the existing urban operational analyst covers only two layers of information, without fixing a lot of little things, random combination that often leads to first level problems. Modern metropolis watch pipe breaks, traffic accidents, damage to motor and power grid infrastructure in near real-time. But they still have to learn about a delay fault in the related and supporting systems.
For example, the February accident in the New York subway, when a crowded train collided at the intersection of train tracks with a sporty SUV, could not happen if the city service fixed the broken grate in the move in time (during the day), limiting the danger of leaving the side with poor visibility. And there’s more than a hundred of such exemplars in the study.
Cars are going to gather detailed information for city analysts soon, believes Doctoroff. It will not be videos – the system simply cannot store and process so much information from the millions of cars. The developers of the task instantly convert visual information into the program signal: the camera will send set of impersonal codes to the data center, and on this basis, the system is going to understand which parts in the environment and infrastructure of the metropolis were changed.
Google X Lab, which is engaged in the systems, including recognition of images, will help to turn "heavy" picture to the "light" code. And according to the developers, taxi services and public transportation management services are the first to equip their cars with microcams.
Another potential application of artificial intelligence in the cities - urban planning system. Doctoroff believes that the long-term experience in the development of master plans and already accumulated statistics allow you to create algorithms identifying and defining the trends of development of cities more accurately than architects.
Professionals already use complex programs and systems to handle large data now, but technology companies have the task of another scale: the urban environment should be extremely transparent - at the level of all available interface to provide answers to any questions related to the growth of the city to all participants interested in the process (investors, developers, builders, owners of commercial real estate, cultural projects, transport companies etc.).
- The city does not know and does not see himself, but we almost did not see the city as well - says Doctoroff.
- Today, even the most advanced systems take a few years to decide on development of the territory, since they require a thorough analysis of a long and full compliance with the general plan. The result is a radical-synchronization: the city is naturally turned to one side, the investor looks to the other, because he still has the information a year old. Business and government are losing out on billions of-synchronization. Most of the unsuccessful development projects are partially or fully explained by it. As no research institute will not be able to expedite the process, we need technological solutions.
At first glance, the idealistic concept has long been discussed in a professional environment. At its core - the desire to abandon the conservative format of the general plan, which is set in the years ahead and, accordingly, cannot take into account the numerous changes in the economy, society and technology. The master plan is adaptive in the case of the "city of big data", its strategic objectives do not change, but specific directions are formed not by hands but the computer. Thus, the general plan, Google believes, becomes a joint product of artificial intelligence and collective initiatives.
On a practical level, this may look like a public portal with a dynamic map, which shows all the processes that occur in the urban environment, space and territory in real time. Actors send project proposals, the system considers and processes them, making up the corrected long-term plans based on the incoming information.
Perhaps now, only Google is able to implement both the plan and the software system based on artificial intelligence, as well as the public interface.
It is believed that the project of unmanned vehicles, which Google develops, is aimed primarily at private mass market. In fact, the company never promised that it’s going to begin with the individual cars, and initially counted on the evolutionary transition from the general to the particular.
One of Sidewalk Labs’s fields, apparently, will be the development of public transport unmanned system. Google cars’ quality control is already very high, but it is too early to leave the pilot zones. For example, the Google cars cannot move in the pouring rain and the snow-covered areas. Identification of the area takes place after comparing pre-captured images with the results of visualization of the surrounding landscape taken by car scanning system, that is, in a normal situation, the system can tell apart an usual pedestrian from a telegraph pole, but it’s powerless in the conditions of bad weather. In addition, the cars are unable to recognize the temporary traffic signals, they cannot distinguish pedestrians from police or crumpled paper from the stone, and not least, do not know how to park.
Google plans to correct these deficiencies only in 2020, but Doctoroff believes that the current level of development is ideal for transport systems with clear limitations: the predetermined route, prescribed cycle, often physically delimited field of motion, etc.
The company has the technology, but judging by how short-spoken the company is, Sidewalk Labs’ main task in the near future will not be as much to market specific solutions as their business case. Doctoroff believes that unmanned transportation are ultimately more profitable even with the traditional costs of production of innovative, conservative citizens and other costs. Yet, it has to be proved.
source: forbes.com
