Google falls to third place in worldwide smart speaker market – gpgmail


The global smart speaker market grew 55.4% in the second quarter to reach 26.1 million shipments, according to a new report from Canalys. Amazon continued to lead the race, accounting for 6.6 million units shipped in the quarter. Google, however, fell to the third spot as China’s Baidu surged ahead. Baidu in Q2 grew a sizable 3,700% to reach 4.5 million units, overtaking Google’s 4.3 million units shipped.

China’s market overall doubled its quarterly shipments to 12.6 million units, or more than twice the U.S.’s 6.1 million total. The latter represents a slight (2.4%) decline since the prior quarter.

Baidu’s growth in the quarter was attributed to aggressive marketing and go-to-market campaigns. It was particularly successful in terms of smart displays, which accounted for 45% of the products it shipped.

“Local network operators’ interests on the [smart display] device category soared recently. This bodes well for Baidu as it faces little competition in the smart display category, allowing the company to dominate in the operator channel,” noted Canalys Research Analyst Cynthia Chen.

Meanwhile, Google was challenged by the Nest rebranding in Q2, the analyst firm said.

The report also suggested that Google should introduce a revamped smart speaker portfolio to rekindle consumer interest. The Google Home device hasn’t been updated since launch — still sporting the air freshener-style looks it had back in 2016. And the Google Home mini hasn’t received much more than a color change.

Instead, Google’s attention as of late has been on making it easier for device manufacturers to integrate with Google Assistant technology, in addition to its increased focus on smart displays.

Amazon, by comparison, has updated its Echo line of speakers several times while expanding Alexa to devices with screens like the Echo Spot and Show, and to those without like the Echo Plus, Echo Dot, Echo Auto and others — even clocks and microwaves — as sort of public experiments in voice computing.

That said, both Amazon and Google turned their attention to non-U.S. markets in Q2, the report found. Indeed, 50% of Amazon’s smart speaker shipments were outside the U.S. in Q2, up from 32% in Q2 last year. And 55% of Google’s shipments were outside the U.S., up from 42% in Q2 2018.

Beyond the top 3 — Amazon, Baidu and now No. 3 Google — the remaining top five included Alibaba and Xiaomi, with 4.1 million and 2.8 million units shipped in Q2, respectively.

The rest of the market, which would also include Apple’s HomePod, totaled 3.7 million units.


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The renaissance of silicon will create industry giants – gpgmail


Every time we binge on Netflix or install a new internet-connected doorbell to our home, we’re adding to a tidal wave of data. In just 10 years, bandwidth consumption has increased 100 fold, and it will only grow as we layer on the demands of artificial intelligence, virtual reality, robotics and self-driving cars. According to Intel, a single robo car will generate 4 terabytes of data in 90 minutes of driving. That’s more than 3 billion times the amount of data people use chatting, watching videos and engaging in other internet pastimes over a similar period.

Tech companies have responded by building massive data centers full of servers. But growth in data consumption is outpacing even the most ambitious infrastructure build outs. The bottom line: We’re not going to meet the increasing demand for data processing by relying on the same technology that got us here.

The key to data processing is, of course, semiconductors, the transistor-filled chips that power today’s computing industry. For the last several decades, engineers have been able to squeeze more and more transistors onto smaller and smaller silicon wafers — an Intel chip today now squeezes more than 1 billion transistors on a millimeter-sized piece of silicon.

This trend is commonly known as Moore’s Law, for the Intel co-founder Gordon Moore and his famous 1965 observation that the number of transistors on a chip doubles every year (later revised to every two years), thereby doubling the speed and capability of computers.

This exponential growth of power on ever-smaller chips has reliably driven our technology for the past 50 years or so. But Moore’s Law is coming to an end, due to an even more immutable law: material physics. It simply isn’t possible to squeeze more transistors onto the tiny silicon wafers that make up today’s processors.

Compounding matters, the general-purpose chip architecture in wide use today, known as x86, which has brought us to this point, isn’t optimized for computing applications that are now becoming popular.

That means we need a new computing architecture. Or, more likely, multiple new computer architectures. In fact, I predict that over the next few years we will see a flowering of new silicon architectures and designs that are built and optimized for specialized functions, including data intensity, the performance needs of artificial intelligence and machine learning and the low-power needs of so-called edge computing devices.

The new architects

We’re already seeing the roots of these newly specialized architectures on several fronts. These include Graphic Processing Units from Nvidia, Field Programmable Gate Arrays from Xilinx and Altera (acquired by Intel), smart network interface cards from Mellanox (acquired by Nvidia) and a new category of programmable processor called a Data Processing Unit (DPU) from Fungible, a startup Mayfield invested in.  DPUs are purpose-built to run all data-intensive workloads (networking, security, storage) and Fungible combines it with a full-stack platform for cloud data centers that works alongside the old workhorse CPU.

These and other purpose-designed silicon will become the engines for one or more workload-specific applications — everything from security to smart doorbells to driverless cars to data centers. And there will be new players in the market to drive these innovations and adoptions. In fact, over the next five years, I believe we’ll see entirely new semiconductor leaders emerge as these services grow and their performance becomes more critical.

Let’s start with the computing powerhouses of our increasingly connected age: data centers.

More and more, storage and computing are being done at the edge; that means, closer to where our devices need them. These include things like the facial recognition software in our doorbells or in-cloud gaming that’s rendered on our VR goggles. Edge computing allows these and other processes to happen within 10 milliseconds or less, which makes them more work for end users.

I commend the entrepreneurs who are putting the silicon back into Silicon Valley.

With the current arithmetic computations of x86 CPU architecture, deploying data services at scale, or at larger volumes, can be a challenge. Driverless cars need massive, data-center-level agility and speed. You don’t want a car buffering when a pedestrian is in the crosswalk. As our workload infrastructure — and the needs of things like driverless cars — becomes ever more data-centric (storing, retrieving and moving large data sets across machines), it requires a new kind of microprocessor.

Another area that requires new processing architectures is artificial intelligence, both in training AI and running inference (the process AI uses to infer things about data, like a smart doorbell recognizing the difference between an in-law and an intruder). Graphic Processing Units (GPUs), which were originally developed to handle gaming, have proven faster and more efficient at AI training and inference than traditional CPUs.

But in order to process AI workloads (both training and inference), for image classification, object detection, facial recognition and driverless cars, we will need specialized AI processors. The math needed to run these algorithms requires vector processing and floating-point computations at dramatically higher performance than general purpose CPUs provide.

Several startups are working on AI-specific chips, including SambaNova, Graphcore and Habana Labs. These companies have built new AI-specific chips for machine intelligence. They lower the cost of accelerating AI applications and dramatically increase performance. Conveniently, they also provide a software platform for use with their hardware. Of course, the big AI players like Google (with its custom Tensor Processing Unit chips) and Amazon (which has created an AI chip for its Echo smart speaker) are also creating their own architectures.

Finally, we have our proliferation of connected gadgets, also known as the Internet of Things (IoT). Many of our personal and home tools (such as thermostats, smoke detectors, toothbrushes and toasters) operate on ultra-low power.

The ARM processor, which is a family of CPUs, will be tasked for these roles. That’s because gadgets do not require computing complexity or a lot of power. The ARM architecture is perfectly designed for them. It’s made to handle smaller number of computing instructions, can operate at higher speeds (churning through many millions of instructions per second) and do it at a fraction of the power required for performing complex instructions. I even predict that ARM-based server microprocessors will finally become a reality in cloud data centers.

So with all the new work being done in silicon, we seem to be finally getting back to our original roots. I commend the entrepreneurs who are putting the silicon back into Silicon Valley. And I predict they will create new semiconductor giants.


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Amazon’s lead EU data regulator is asking questions about Alexa privacy – gpgmail


Amazon’s lead data regulator in Europe, Luxembourg’s National Commission for Data Protection, has raised privacy concerns about its use of manual human reviews of Alexa AI voice assistant recordings.

A spokesman for the regulator confirmed in an email to gpgmail it is discussing the matter with Amazon, adding: “At this stage, we cannot comment further about this case as we are bound by the obligation of professional secrecy.” The development was reported earlier by Reuters.

We’ve reached out to Amazon for comment.

Amazon’s Alexa voice AI, which is embedded in a wide array of hardware — from the company’s own brand Echo smart speaker line to an assortment of third party devices (such as this talkative refrigerator or this oddball table lamp) — listens pervasively for a trigger word which activates a recording function, enabling it to stream audio data to the cloud for processing and storage.

However trigger-word activated voice AIs have been shown to be prone to accidental activation. While a device may be being used in a multi-person household. So there’s always a risk of these devices recording any audio in their vicinity, not just intentional voice queries…

In a nutshell, the AIs’ inability to distinguish between intentional interactions and stuff they overhear means they are natively prone to eavesdropping — hence the major privacy concerns.

These concerns have been dialled up by recent revelations that tech giants — including Amazon, Apple and Google — use human workers to manually review a proportion of audio snippets captured by their voice AIs, typically for quality purposes. Such as to try to improve the performance of voice recognition across different accents or environments. But that means actual humans are listening to what might be highly sensitive personal data.

Earlier this week Amazon quietly added an option to the settings of the Alexa smartphone app to allow users to opt out of their audio snippets being added to a pool that may be manually reviewed by people doing quality control work for Amazon — having not previously informed Alexa users of its human review program.

The policy shift followed rising attention on the privacy of voice AI users — especially in Europe.

Last month thousands of recordings of users of Google’s AI assistant were leaked to the Belgian media which was able to identify some of the people in the clips.

A data protection watchdog in Germany subsequently ordered Google to halt manual reviews of audio snippets.

Google responded by suspending human reviews across Europe. While its lead data watchdog in Europe, the Irish DPC, told us it’s “examining” the issue.

Separately, in recent days, Apple has also suspended human reviews of Siri snippets — doing so globally, in its case — after a contractor raised privacy concerns in the UK press over what Apple contractors are privy to when reviewing Siri audio.

The Hamburg data protection agency which intervened to halt human reviews of Google Assistant snippets urged its fellow EU privacy watchdogs to prioritize checks on other providers of language assistance systems — and “implement appropriate measures” — naming both Apple and Amazon.

In the case of Amazon, scrutiny from European watchdogs looks to be fast dialling up.

At the time of writing it is the only one of the three tech giants not to have suspended human reviews of voice AI snippets, either regionally or globally.

In a statement provided to the press at the time it changed Alexa settings to offer users an opt-out from the chance of their audio being manually reviewed, Amazon said:

We take customer privacy seriously and continuously review our practices and procedures. For Alexa, we already offer customers the ability to opt-out of having their voice recordings used to help develop new Alexa features. The voice recordings from customers who use this opt-out are also excluded from our supervised learning workflows that involve manual review of an extremely small sample of Alexa requests. We’ll also be updating information we provide to customers to make our practices more clear.


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