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Archivo del Autor: Belen De Leon

Huawei Mate X foldable phone probably won't come out before November – CNET

The foldable phone reportedly won’t arrive in September after all.
Source: CNET

Toyota recalls 2019 Prius, RAV4 Hybrid for faulty brake assist – Roadshow

A handful of Lexus hybrids are also part of the recall.
Source: CNET

Netflix's To All The Boys sequel gets its release date – CNET

A third film is already in production.
Source: CNET

La primera ley anti-CRISPR le prohíbe editar su propio genoma en casa

Un nuevo proyecto de ley de California exige que los kits de edición genética para el consumidor incluyan una advertencia que indique que no están diseñados para el autoconsumo. Sin embargo, todavía no existe ningún producto así
Source: MIT

Google publica un vídeojuego para que la IA aprenda a jugar al fútbol

El equipo de inteligencia artificial del gigante ha lanzado ‘Google Research Football Environment’, un entorno con varios niveles de complejidad que cualquier investigador puede usar para entrenar a sus algoritmos a lidiar con lo imprevisible
Source: MIT

Judge bars Georgia from using current voting technology in 2020 – CNET

The state is in the midst of getting new voting machines and other tech.
Source: CNET

Grab this latest Apple iPad Wi-Fi 32GB with an $81 discount at Walmart

If you’re in the market for the newest Apple iPads, Walmart has offered a 25% discount on the latest Apple iPad Wi-Fi 32GB variant. Order yours now for only $249 instead of $330, and save as much as $81.
Source: Digital trends

These robo-shorts are the precursor to a true soft exoskeleton

When someone says “robotic exoskeleton,” the power loaders from Aliens are what come to mind for most people (or at least me), but the real things will be much different: softer, smarter and used for much more ordinary tasks. The latest such exo from Harvard is so low-profile you could wear it around the house.

Designed by researchers at Harvard’s Wyss Institute (in collaboration with several other institutions), which focuses on soft robotics and bio-inspired mechanisms, the exosuit isn’t for heavy lifting or combating xenomorphs, but simply walking and running a little bit more easily.

The suit, which is really more of a pair of shorts with a mechanism attached at the lower back and cables going to straps on the legs, is intended to simply assist the leg in its hip-extension movement, common to most forms of locomotion.

An onboard computer (and neural network, naturally) detects the movements of the wearer’s body and determines both the type of gait (walking or running) and what phase of that gait the leg is currently in. It gives the leg making the movement a little boost, making it just that much easier to do it.

In testing, the suit reduced the metabolic load of walking by 9.3% and running by 4%. That might not sound like much, but they weren’t looking to create an Olympic-quality cyborg — just show reliable gains from a soft, portable exosuit.

“While the metabolic reductions we found are modest, our study demonstrates that it is possible to have a portable wearable robot assist more than just a single activity, helping to pave the way for these systems to become ubiquitous in our lives,” said lead study author Conor Walsh in a news release.

The whole idea, then, is to leave behind the idea of an exosuit as a big mechanical thing for heavy industry or work, and bring in the idea that one could help an elderly person stand up from a chair, or someone recovering from an accident walk farther without fatigue.

rt scitoc aug16 r1

The whole device, shorts and all, weighs about 5 kilograms, or 11 pounds. Most of that is in the little battery and motor pack stashed at the top of the shorts, near the body’s center of mass, helping it feel lighter than it is.

Of course, this is the kind of thing the military is very interested in — not just for active duty (a soldier who can run twice as far or fast) but for treatment of the wounded. So it shouldn’t be a surprise that this came out of a DARPA project initiated years ago (and ongoing in other forms).

But by far the more promising applications are civilian, in the medical field and beyond. “We are excited to continue to apply it to a range of applications, including assisting those with gait impairments, industry workers at risk of injury performing physically strenuous tasks, or recreational weekend warriors,” said Walsh.

Currently the team is hard at work improving the robo-shorts, reducing the weight, making the assistance more powerful and more intuitive and so on. The paper describing their system was the cover story of this week’s edition of the journal Science.


Source: TechCrunch

How Facebook Catches Bugs in Its 100 Million Lines of Code

For the past four years, Facebook has quietly used a homegrown tool called Zoncolan to find bugs in its massive codebase.
Source: Wired

The renaissance of silicon will create industry giants

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.


Source: TechCrunch

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