Showing posts with label Artificial Intelligence. Show all posts
Showing posts with label Artificial Intelligence. Show all posts

Friday, 26 October 2018

The Yin and the Yang of AI & Blockchain


Today I read about HTC's Exodus 1, new Blockchain smartphone that only people with crypto-currency can buy. SCMP described in very simple terms what this phone is for:

Both HTC’s Exodus and Sirin’s Finney smartphones feature a built-in digital wallet application that will enable users to securely store and use cryptocurrencies, such as bitcoin and ethereum, in daily transactions.

Those smartphones are designed to replace the special memory sticks, which employ complex usernames and passwords to access, that cryptocurrency investors use to store their digital money. These investors typically store most of their cryptocurrencies in such hardware, which are kept offline as a means of security.

“There are things that a phone manufacturer can do with a chip that nobody else can,” said Chen. “We want to be safer than the existing hardware wallets … HTC has a track record of making trusted hardware.”

The company’s Exodus smartphone, for example, can serve as a “node”, which can connect to certain blockchain networks to enable trading of tokens between users. It will also be able to act as a so-called mining rig for users to earn new tokens tied to the Exodus blockchain.

“At some point, we’ll do our own utility token,” said Chen, adding that there was no timetable for such a token release.

HTC’s foray into blockchain, the distributed ledger technology behind cryptocurrencies like bitcoin, represents a strategy to keep the company relevant in smartphones, which is a market dominated by Samsung Electronics and Apple, followed by Huawei Technologies, Xiaomi and other major Chinese brands.

Anyway, the blockchain smartphone reminds me of the joke above (via marketoonist). The second technology mentioned in this joke is AI or Artificial Intelligence.

I heard HP Enterprise talk about AI recently and this picture above is a nice simple way to show how Deep Learning (DL), Artificial Neural Networks (ANN), Machine Learning (ML) and Artificial Intelligence (AI) are related.

I see AI and blockchain often referred to together. This does not necessarily mean that they are related. iDate allowed me to share a recent presentation (embedded below) that refers to AI & blockchain as Yin and Yang. Anyway, I am happy to learn more so if you have any thoughts please feel free to share.



Further Reading:


Related Posts:

Tuesday, 2 October 2018

Benefits and Challenges of Applying Device-Level AI to 5G networks


I was part of Cambridge Wireless CWTEC 2018 organising committee where our event 'The inevitable automation of Next Generation Networks' covered variety of topics with AI, 5G, devices, network planning, etc. The presentations are available freely for a limited period here.

One of the thought provoking presentations was by Yue Wang from Samsung R&D. The presentation is embedded below and can be downloaded from Slideshare.



This presentation also brought out some interesting thoughts and discussions:

  • While the device-level AI and network-level AI would generally work cooperatively, there is a risk that some vendor may play the system to make their devices perform better than the competitors. Something similar to the signaling storm generated by SCRI (see here).
  • If the device-level and network-level AI works constructively, an operator may be able to claim that their network can provide a better battery life for a device. For example iPhone XYZ has 25% better battery life on our network rather than competitors network.
  • If the device-level and network-level AI works destructively for any reason then the network can become unstable and the other users may experience issues. 

I guess all these enhancements will start slowly and there will be lots of learning in the first few years before we have a stable, mutually beneficial solution.

Related Posts:

Monday, 13 August 2018

Telefonica: Big Data, Machine Learning (ML) and Artificial Intelligence (AI) to Connect the Unconnected


Earlier, I wrote a detailed post on how Telefonica was on a mission to connect 100 Million Unconnected with their 'Internet para todos' initiative. This video below is a good advert of what Telefinica is trying to achieve in Latin America


I recently came across a LinkedIn post on how Telef√≥nica uses AI / ML to connect the unconnected by Patrick Lopez, VP Networks Innovation @ Telefonica. It was no brainer that this needs to be shared.



In his post, Patrick mentions the following:

To deliver internet in these environments in a sustainable manner, it is necessary to increase efficiency through systematic cost reduction, investment optimization and targeted deployments.

Systematic optimization necessitates continuous measurement of the financial, operational, technological and organizational data sets.

1. Finding the unconnected


The first challenge the team had to tackle was to understand how many unconnected there are and where. The data set was scarce and incomplete, census was old and population had much mobility. In this case, the team used high definition satellite imagery at the scale of the country and used neural network models, coupled with census data as training. Implementing visual machine learning algorithms, the model literally counted each house and each settlement at the scale of the country. The model was then enriched with crossed reference coverage data from regulatory source, as well as Telefonica proprietary data set consisting of geolocalized data sessions and deployment maps. The result is a model with a visual representation, providing a map of the population dispersion, with superimposed coverage polygons, allowing to count and localize the unconnected populations with good accuracy (95% of the population with less than 3% false positive and less than 240 meters deviation in the location of antennas).


2. Optimizing transport



Transport networks are the most expensive part of deploying connectivity to remote areas. Optimizing transport route has a huge impact on the sustainability of a network. This is why the team selected this task as the next challenge to tackle.

The team started with adding road and infrastructure data to the model form public sources, and used graph generation to cluster population settlements. Graph analysis (shortest path, Steiner tree) yielded population density-optimized transport routes.


3. AI to optimize network operations


To connect very remote zones, optimizing operations and minimizing maintenance and upgrade is key to a sustainable operational model. This line of work is probably the most ambitious for the team. When it can take 3 hours by plane and 4 days by boat to reach some locations, being able to make sure you can detect, or better, predict if / when you need to perform maintenance on your infrastructure. Equally important is how your devise your routes so that you are as efficient as possible. In this case, the team built a neural network trained with historical failure analysis and fed with network metrics to provide a model capable of supervising the network health in an automated manner, with prediction of possible failure and optimized maintenance route.

I think that the type of data driven approach to complex problem solving demonstrated in this project is the key to network operators' sustainability in the future. It is not only a rural problem, it is necessary to increase efficiency and optimize deployment and operations to keep decreasing the costs.


Finally, its worth mentioning again that I am helping CW (Cambridge Wireless) organise their annual CW TEC conference on the topic 'The inevitable automation of Next Generation Networks'. There are some good speakers and we will have similar topics covered from different angles, using some other interesting approaches. The fees are very reasonable so please join if you can.

Related posts:

Tuesday, 13 February 2018

Artificial Intelligence - Beyond SON for Autonomous Networks


What is the next step in evolution of SON? Artificial Intelligence obviously. The use of artificial intelligence (AI) techniques in the network supervisory system could help solve some of the problems of future network deployment and operation. ETSI has therefore set up a new 'Industry Specification Group' on 'Experiential Networked Intelligence' (ISG ENI) to develop standards for a Network Supervisory assistant system.


The ISG ENI focuses on improving the operator experience, adding closed-loop artificial intelligence mechanisms based on context-aware, metadata-driven policies to more quickly recognize and incorporate new and changed knowledge, and hence, make actionable decisions. ENI will specify a set of use cases, and the generic technology independent architecture, for a network supervisory assistant system based on the ‘observe-orient-decide-act’ control loop model. This model can assist decision-making systems, such as network control and management systems, to adjust services and resources offered based on changes in user needs, environmental conditions and business goals.


The introduction of technologies such as Software-Defined Networking (SDN), Network Functions Virtualisation (NFV) and network slicing means that networks are becoming more flexible and powerful. These technologies transfer much of the complexity in a network from hardware to software, from the network itself to its management and operation. ENI will make the deployment of SDN and NFV more intelligent and efficient and will assist the management and orchestration of the network.


We expect to complete the first phase of ENI work in 2019. It will include a description of use cases and requirements and terminology, including a definition of features, capabilities and policies, which we will publish in a series of informative best practice documents (Group Reports (GRs)).
This will of course require co-operation from many different industry bodies including GSMA, ITU-T, MEF, IETF, etc.

Will see how this goes.

Further reading: