Difficult times for operators call for questioning old orthodoxies to win. For the better part of a decade, telecom companies have suffered through declining revenues, cash flow, and return on investment just as tech companies like Google, Facebook, Amazon, and others have mushroomed by building their businesses on the operators' own infrastructure. While these tech visionaries have enjoyed well over $1 trillion in combined market-cap growth by innovating and thinking differently and adeptly, telecom companies have tried to compete by implementing the same old survival tactics: cutting costs, reducing the workforce, and timidly entering into new business adjacencies. The trouble is that playbook no longer applies. It's time the telecom companies embrace this new reality and rethink the key orthodoxies that have shaped their industry since the first phone call was made about 140 years ago. If not, the alternative is dire.

Businesses have long been told to think outside the box--but what about thinking outside the industry? Artificial intelligence (AI) and robotics have brought dramatic improvements in efficiency and overall quality to a wide variety of sectors, including automotive and e-commerce. Can they change the game for telecom companies, too? The idea that what's good for General Motors is good for telcos might seem a bit unorthodox. They are, after all, very different businesses. Yet this year's telco IT benchmarking study (TeBIT)--a survey of European operators' IT spending and performance that was completed in August 2017--suggests that some telcos, at least, are beginning to see how AI and robotics could benefit them.

It's been over six weeks since Hurricane Maria devastated Puerto Rico, leaving millions without power or access to reliable communication. The storm clobbered the island's communications infrastructure--after all, cell towers can't produce a signal without power, and backup batteries only last hours. The hurricane damaged the above-ground fiber lines that connect towers to the main network, too. AT&T, for example, is using stop-gap measures like portable cell towers on trucks to help get the network back up, and says that 70 percent of the population there is now covered by their network. And then there are the balloons.

Neural Networks and Machine Learning are some of this year's biggest buzzwords in the world of smartphone processors. Huawei's HiSilicon Kirin 970, Apple's A11 Bionic, and the image processing unit (IPU) inside the Google Pixel 2 all boast dedicated hardware support for this emerging technology. The trend so far has suggested that machine learning requires a dedicated piece of hardware, like a Neural Processing Unit (NPU), IPU, or "Neural Engine", as Apple would call it. However, the reality is these are all just fancy words for custom digital signal processors (DSP) -- that is, hardware specialized in performing complex mathematical functions quickly. Today's latest custom silicon has been specifically optimized around machine learning and neural network operations, the most common of which include dot product math and matrix multiply.

According to the latest research from Counterpoint's Components Tracker Service, one in three smartphones to be shipped in 2020 will natively embed machine learning and artificial intelligence (AI) capabilities at the chipset level. Apple, with its Bionic system on chip (SoC), proliferating across its complete portfolio over the next couple of years, will drive native AI adoption in smartphones. Its universal adoption of AI-capable SoCs will likely enable Apple to lead the AI-capable chip market through 2020. Huawei, with its HiSilicon Kirin 970 SoC, launched in September and finding application in the Huawei Mate 10 series launched today in Munich, is second to market after Apple with AI-capable smartphones. The Huawei Mate 10 is able to accomplish diverse computational tasks efficiently, thanks to the neural processing unit at the heart of the Kirin 970 SoC.

There are few people who we can genuinely say are keen on bringing about the so-called technological singularity --i.e., that moment when intelligent machines become smarter than human beings. There's famed "future teller" and Google top engineer Ray Kurzweil, who's said the singularity isn't something to be feared. But, perhaps, no one is as dedicated to making the singularity happen as much as Masayoshi Son, CEO of Japanese telecommunications giant SoftBank. Son recently spoke at the Future Investment Initiative held in Riyadh, Saudi Arabia, where he mentioned that the singularity might just happen in about 30 years, when artificial intelligence develops an IQ of 10,000 --and SoftBank's invested $100 billion to build chips capable of such IQ levels. That's well beyond what's considered average by human standards, which is at 100, and even greater than a human genius with a 200 IQ.

Robots will be 100 times more intelligent than the average human in 30 years, the CEO of tech giant SoftBank has claimed. Billionaire tech mogul Masayoshi Son, 60, said that by 2047 artificial intelligence (AI) will have reached an IQ of 10,000. By comparison, the average human IQ is 100, while anything over 140 is a'genius' score. Mensa, the'high IQ society', only accepts members with a score above 130. Speaking at the Future Investment Initiative in Riyadh, Saudi Arabia, on Wednesday, Mr Son said: 'This is the first time ... the tool becomes smarter than ourselves.'

We present a new model-based integrative method for clustering objects given both vectorial data, which describes the feature of each object, and network data, which indicates the similarity of connected objects. The proposed general model is able to cluster the two types of data simultaneously within one integrative probabilistic model, while traditional methods can only handle one data type or depend on transforming one data type to another. Bayesian inference of the clustering is conducted based on a Markov chain Monte Carlo algorithm. A special case of the general model combining the Gaussian mixture model and the stochastic block model is extensively studied. We used both synthetic data and real data to evaluate this new method and compare it with alternative methods. The results show that our simultaneous clustering method performs much better. This improvement is due to the power of the model-based probabilistic approach for efficiently integrating information.

Every piece of IT hardware runs on software, make no mistake. For the new era of mobile handheld devices to become truly smart smartphones, they will need to exhibit new software processing powers over and above the ability to'simply' take photos, play games and surf the web. To be clear, the next era of smart smartphones will feature an increasing amount of machine learning and Artificial Intelligence (AI). This assertion (or is it almost a truism?) is at the heart of Chinese telecommunications giant's strategy. The firm has (literally) etched AI into the DNA of the'chipset' of its new line of Mate Series devices running on the new Kirin 970, a product described as the first AI processor for smartphones with a dedicated Neural Network Processing Unit (NPU) i.e. neural being'like a brain'.

What are they actually good for? In the recent months we've heard a lot about specialized silicon being used for machine learning in mobile devices. Apple's new iPhones have their "neural engine"; Huawei's Mate 10 comes with a "neural processing unit"; and companies that manufacture and design chips (like Qualcomm and ARM) are gearing up to supply AI-optimized hardware to the rest of the industry. What's not clear, is how much all this benefits the consumer. When you're buying your phone, should an "AI chip" be on your wish list?