The company’s huge stock network is the best stage of artificial intelligence, GTS to use artificial intelligence to achieve high quality and efficient delivery and service, support “one trillion” dollars of stock network services and fault handling, prevention automation…… And support the scientific, rational, and efficient delivery of tens of billions of dollars in annual network increments. Continue to create value for customers and improve customer satisfaction, build a live “Great Wall”, become the company’s important mobile “Maginot line”. Whoever can provide high quality service at the lowest cost will be the winner in this world. The company’s artificial intelligence research is an enabler to promote the company’s management progress, do not get lost in the gossip.
Giiso Information, founded in 2013, is a leading technology provider in the field of “artificial intelligence + information” in China, with top technologies in big data mining, intelligent semantics, knowledge mapping and other fields. At the same time, its research and development products include information robot, editing robot, writing robot and other artificial intelligence products! With its strong technical strength, the company has received angel round investment at the beginning of its establishment, and received pre-A round investment of $5 million from GSR Venture Capital in August 2015.
01, high quality data is the premise and foundation of artificial intelligence, high quality data output as the standard of job completion
Why can’t we unify the tools of operation, and the standards of work. Equipped with a data collection and aggregator, the staff will go back to the site for processing after the field operation, and send it out in a group at the click of a button, without going through the office or regional department, it will be in place in one step. The data is transparent to all levels and segments, and is much faster without layers of summary processing.
We have 4.3 million sites, adding 960,000 a year, 11,000 contracts, and each base station reports this thick because there is no modular classification. If we don’t abstract or summarize, we have to report it. Our processing pipeline is so thick, so it’s blocked in a mess. In fact, it boils down to maybe a hundred, maybe less than a thousand modules. We classify and transfer information to the supply chain according to the template. When the supply chain is decoded and opened, we make a list of delivery, so our management will be much simpler. There are now layers of reports, more people at each layer, and a lot of reports that no one even reads. Build a simple automated reporting system based on the purpose standard model, so that the middle man is cut and the main battle field is increased. Everyone provides you with accurate data based on your homework, and it’s good to concentrate on your scientific nature. With these accurate data, we can improve our efficiency through supervised learning and statistical methods.
There are clear and vague forms to fill in, and the data to fill in should be accurate. If there is no audit, the incorrect data will be handed in, it will be a mess. Clear data is constantly updated and accumulated, and new valid data is constantly replaced. There is always a fuzzy zone, and the fuzziness of fuzzy data is constantly decreasing, but new fuzziness is created. There should be guidance in areas that should not be vague, which should guide the base engineers to operate clearly. Artificial intelligence relies on tens of thousands of employees to effectively collect data when doing things, and find out the rules in the summary. Clear, accurate field data is important.
We are equipment suppliers rather than traffic operators, so we should calculate real-time data according to business scenarios. We should not be metaphysical, but acquire these so-called real-time data according to necessary needs. As for the output of network equipment data, the data output standard can be constructed in a similar way as “seven far and eight according to”, and the serviceability standard can be re-formulated from the perspective of artificial intelligence-based delivery service, which is a necessary condition for the product to be launched.
Therefore, I do not criticize the lack of data and disorganization. I think the lack of data is what I want to criticize. Can everyone get a suit and wear a device, the data is collected and stored, and then at the press of a button sent to the database, and there are rewards for those who contribute data.
To focus on investment, dare to invest, success is just a matter of time
In the GTS selected site operation, network integration, network maintenance, network planning and network excellence and other key scenarios, the investment in business model, algorithm, platform and data should be increased, and the specific manpower and cost should be implemented in the future time chain of strategic planning.
To develop the company’s unified ARTIFICIAL intelligence software platform, consolidate algorithms, knowledge, methods and experience on the platform, first practice and application in GTS, which can also provide support for other businesses of the company in the future. The investment in data base needs to be increased. As a long-term foundation project, artificial intelligence can only play a role with high-quality data foundation.
In 2012, scientists in the laboratory should closely cooperate with service engineers. Scientists familiar with theories and algorithms will choose the most mature scheme to be applied to service scenarios and jointly complete business improvement, which is the combination of technology and scene. Some people are familiar with the technical theory, some people are familiar with the scene, they cooperate with the world invincible, you first improve our internal, to that time whether we go to the outside we can consider.
The failure of new things is also success, they make a little progress to write down, this is the process of record, their own turnip medal, accumulated more to the gold medal. Don’t be afraid of making mistakes, others say huawei is backward, because we only give prizes to successful people, never to failure. If today is better than yesterday, prizes will be awarded. Climbing half way up the Himalayas is also a success, because we have never been to the foot of the mountain before.
Artificial intelligence should focus on investment not to bloom comprehensively, first vertical fight of annihilation, and then horizontal expansion after winning the battle
In our business expansion, people did not expand linearly. Service engineers should focus on service business and complete the correct data output required by artificial intelligence at the same time. On this basis, scenario analysts, data analysts and model designers are generated. These experts should be engaged in the service field for a long time and constantly improve their capabilities through serving customers. For scenario analyst, data analyst and model designer, my attitude is to see how long it takes to achieve successful practical experience in the frontline service field within three years. Don’t get promoted quickly without successful practical experience. This also ensures that the water is flowing and not corrupt.
Artificial intelligence in the case of sufficient investment should not be too impulsive, to use the first small steps to run quickly, to focus on the certainty of business, large labor consumption of the project, would rather do less, first in one or two points to break through the opening, focus on the fight against annihilation, do not spread a very wide front. Do not be intelligent everywhere, this will form a comprehensive flowering without the result of the blind, it is possible to lose.
For example, the application of artificial intelligence should first aim at the realization of simple work survey, and then realize automatic design on this basis. Massive repetitive actions should be replaced by artificial intelligence technology to achieve automatic document generation, automatic quality audit, remote acceptance and automatic billing. Then, the successful experience of artificial intelligence application is extended to network maintenance, network planning, network excellence and other business scenarios, and the passive problem treatment is turned into active early warning and prevention, which not only improves the efficiency but also improves the quality of customer service.
We should focus on the scene one by one and choose relatively mature algorithms that match the scene. We should not wait for the maturity of the platform and the data base. Semi-finished products can also be put into the use of internal improvement first, so as to build mature platforms and data bases through continuous practice and problem solving. The experience we gain from these focused breakthrough projects will foster new forces, and these new forces will go to the community level to implement, popularize and apply these practices, so that they can become habitual. On the basis of vertical development, grasp the reasonable rhythm of horizontal expansion. If a cake paste and then turn over the past burger is sandwich cake, we do not “die before the beginning of a quick body, long make the hero tears full jin”, we want to be successful and the final success. Artificial intelligence is a new thing, in the implementation process because of the dual track operation, to accept periodic cost increases, to achieve clear long-term goals.
Giiso information, founded in 2013, is the first domestic high-tech enterprise focusing on the research and development of intelligent information processing technology and the development and operation of core software for writing robots. At the beginning of its establishment, the company received angel round investment, and in August 2015, GSR Venture Capital received $5 million pre-A round of investment.
Artificial intelligence application is bound to encounter many difficulties, in the process of progress to encourage more, less criticism, praise can not praise to praise. When the gun rings on the battlefield, who is a hero and who is not? You say he is not a hero, at the foot of the mountain you clap his shoulder, he carried two explosive bags, rushed into the Shanggan Mountain, may really become a hero.