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?

Huawei (pronounced "Wah-way") is the world's largest telecommunications company. A member of the Fortune Global 100, it is also the world's third-largest seller of smartphones. Just one day after its launch of the world's first Artificial Intelligence embedded smartphone, the Mate 10, in Munich, Germany, Huawei hosted the Ultra Broadband Forum in Hangzhou, China. It attracted attendees from over sixty-five countries. I was invited to the conference as a key opinion leader and Huawei partner.

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?

Microsoft is delivering the world's first fully neural on device translations in the Microsoft Translator app for Android, customized for the Huawei Mate 10 series. Microsoft achieved this breakthrough by partnering with Huawei to customize Microsoft's new neural technology for Huawei's new NPU (Neural Processing Unit) hardware. This results in dramatically better and faster offline translations as compared to existing offline packs. The Microsoft Translator app with these capabilities comes pre-installed on Huawei Mate 10 devices allowing every Mate 10 user to have native access to online quality level translations even when they are not connected to the Internet. Until now, due to the computational requirements of neural machine translation, it was not possible to do full Neural Machine Translation (NMT) on-device.