Wong's Gathot: computers

Showing posts with label computers. Show all posts

Wednesday, December 18, 2013

Lenovo ThinkPad Twist review: A great business tool

Lenovo’s ThinkPad Twist is the latest in a string of Windows 8-running tablet-laptop hybrids, and it’s a little different from the competition. Mainly, it’s a business-oriented tablet-laptop (excuse me, tablet-Ultrabook) hybrid that stays true (sort of) to the ThinkPad line’s traditional, if somewhat boring, aesthetic.

Like other tablet-Ultrabook hybrids, the Twist has a unique way of converting itself from a tablet to a laptop and back again. This time the screen is attached to the bottom of the laptop with a single, sturdy rotating hinge. You can rotate the laptop’s screen 180 degrees, and then fold it backward to use it as a tablet. This isn’t a new concept – we actually first saw this style of convertible tablet-laptop way back in the early 2000s when Microsoft was trying to make pre-iPad tablet computers a thing – but it’s implemented much better than what we’ve seen before.

The Thinkpad Twist mounts its display on a rotating hinge.

Our review model, which costs $899.99 as configured, has a third-generation Intel Core i5-3317U processor, 4GB of RAM (3.82GB usable), and a 500GB HDD spinning at 7200rpm alongside a 24GB SSD caching drive. The Twist also has built-in Wi-Fi 802.11a/b/g/n, Bluetooth 4.0, and a slot for a SIM card, for users who want to be connected

Performance

In PCWorld’s WorldBench 8 tests, the ThinkPad Twist scores 47 out of 100. This means that it’s 53 percent slower than our testing model, which is no surprise – our testing model has a third-generation Intel Core i5 desktop processor, 8GB of RAM, and a discrete Nvidia graphics card. The Twist’s score isn’t great – it’s on the lower side of the systems we’ve tested that have the same processor. For example, Lenovo’s IdeaPad Yoga, which has the same i5-3317U processor and 4GB of RAM, scores 60 out of 100 on WB8. Likewise, the Dell XPS 12 Convertible Touch, another convertible tablet-laptop hybrid, score 64 out of 100. The score differentials are probably because those other systems ship with SSDs instead of rotating hard drives.

The Twist also falls short in our individual performance tests. For example, in the PCMark 7 productivity test, the Twist scores 1099, which is just a little behind the Yoga’s 2115 and the Duo 12’s 2187. Although the Twist does have a 24GB SSD boot drive, it takes longer than other convertible Ultrabooks to start up – 13.4 seconds, which is almost twice as long as the Yoga’s 7.9 seconds and the Duo 12’s 8.8 seconds. It is faster, however, than laptops that do not have SSD boot drives, such as the Toshiba Satellite P854t-S4310 (22.7 seconds) and the Acer Aspire V5-571P-6499 (21.3 seconds).

Graphics performance on the Twist is right about where we expect it to fall, considering it’s an Ultrabook with no discrete graphics card. In our Dirt showdown test (maximum quality settings, 1366 by 768 pixel resolution), the Twist managed 28.8 frames per second, which is on par with the frame rates of both the Yoga (30.1fps) and the Duo 12 (33.3fps) in the same test.

We managed to eke out just three hours and 15 minutes of battery life with the Twist, which is not very good considering the class. Other tablet-Ultrabook hybrids typically get at least five hours (the Yoga got five hours and 37 minutes, while the Duo 12 got four hours and 39 minutes), and some, such as the Samsung XE500T1C-A01, get as much as nine hours.

Design and Usability

The Lenovo ThinkPad Twist looks like a sleeker, sexier version of traditional ThinkPad laptops. Lenovo has been careful to keep its ThinkPad line visually similar, keeping the traditional matte black finish and red accents, though it has been updating the look in subtle ways.

The Twist has a flat, smooth cover made of soft, rubbery material. In the lower left corner there’s a silver Lenovo logo, and in the lower right corner there’s a traditional ThinkPad logo. The ThinkPad logo’s “i” has a red dot, which is actually a light that pulses when the computer is turned on. The cover is very simple, and there’s a thin silver line around the edge.

Inside, the Twist looks a little cluttered. There’s another ThinkPad logo (with another pulsing, red-dotted “i”) in the lower right corner of the wrist rest, which is made of the same soft, rubbery material as the cover. The glossy 12.5-inch touchscreen is surrounded by a thick bezel, and there are a couple of buttons located below it: the Windows 8 button for switching back to the home screen, and volume controls.

The laptop sports a full-size, spill-proof, island-style keyboard with small, rounded keys. The keyboard is comfortable to type on, though the keys are a little slippery. In the middle of the keyboard there’s a small red TrackPoint. The TrackPoint’s corresponding three buttons are located directly below the keyboard, above a small matte touchpad. The touchpad has no discrete buttons, and is instead clickable itself. Both the TrackPoint and the touchpad are comfortable as input devices, and offer up smooth, accurate pointing and easy clicking.

Like other tablet-Ultrabook hybrids, the Twist can be used in several different ways. You can open it up and use it as a laptop, or you can twist the screen around to use it as a tablet. The screen, which is attached to the bottom of the laptop by a small, sturdy hinge, only twists one way, and only 180 degrees. In tablet mode, you can tilt the screen backward and use the bottom of the laptop as a stand, or you can tilt the screen all the way back (flat), and use the Twist as a typical tablet.

The Thinkpad Twist in tablet mode.

The Twist is a bit heavy at 3.48 pounds to use as a tablet, so you probably won’t be using it like that very often. But still, it’s a nice option to have.

Lenovo also advertises a “tent” mode, which is when you twist the screen, tilt it back, and then stand the laptop on its edges to make a tent-like structure. While this mode works well with the Yoga, which has balanced parts, it’s not very effective with the Twist. The Twist’s screen is much slimmer and lighter than the bottom part of the laptop, and so propping it up in tent mode does not seem very sturdy.

The Twist offers up decent port selection, considering it’s a tablet-Ultrabook hybrid. It has a Gigabit Ethernet port, which is very useful for business travelers and not something you usually see on Ultrabooks. It also has two USB 3.0 ports, a Mini-DisplayPort and a Mini-HDMI port, a 4-in-1 card reader, and a Kensington lock slot. There’s a combined headphone/microphone jack, and the power button is located on the right side of the screen, along with a screen lock button for when you’re in tablet mode.

Screen and Speakers

The Twist sports a glossy 12.5-inch IPS touchscreen with a native resolution of 1366 by 768 pixels. This resolution can look a little dated on larger screens, but it’s just fine on the Twist’s screen, and images and text look sharp and crisp. Overall, the Twist’s screen is nice-looking: it’s bright, at 350 nits (the average screen brightness for a laptop is between 200 and 250 nits), which means that you’ll be able to use it outside or in bright situations. Extra brightness is ideal, since the Twist is meant to double as a tablet.

The Twist’s screen offers up excellent contrast and off-axis viewing angles; the only small issue I had with the screen was that colors sometimes seemed a little off. For example, whites occasionally looked a little yellowish, especially when the brightness wasn’t pumped up.

As a touchscreen, the Twist’s screen works very well. It’s responsive and accurate, and multi-touch gestures are smooth – more on par with a tablet than with a laptop. It’s similar to the Yoga’s touchscreen, which is also responsive and smooth.

Video looks and sounds pretty mediocre on the Twist. HD streaming video plays back fairly smoothly, but I did see a lot of artifacting and noise in just about every part of every scene – whether I was watching the animated My Little Pony series, or the dark, action-packed Arrow series.

Audio on the Twist is…interesting. It’s been a long time since I’ve heard laptop speakers that are just kind of blah – not outright awful, but also not in any way good. Here’s the thing: first, the speakers seem to be located in the keyboard, which is just kind of weird. Second, though the sound gets pretty loud (and doesn’t distort, even at the highest volume), it’s just very flat. There doesn’t appear to be any bass or treble happening, and so all audio sounds flat, and a little echo-y. This isn’t too much of an issue if you’re just watching a quick clip, but it’s definitely an issue if you want to listen to music.

Bottom Line

Although the Lenovo ThinkPad Twist has its flaws, it does what it’s designed to do very well. That is, it’s a fantastic business-oriented tablet-Ultrabook hybrid, and it’s a great choice for a business user.

The Twist’s performance is a little on the low side for systems in its class, but it’s nothing to be too concerned about. The twisty screen is particularly useful if you’re working with someone and you want to quickly show them what’s going on on your screen (assuming they’re sitting on your left – the screen only twists one way). And of course, the spill-proof keyboard and mobile data option are great for traveling businesspeople.

Don’t get me wrong – the ThinkPad Twist has some issues, and it’s not designed for entertainment. But if you’re just looking for a business tablet-Ultrabook hybrid, then this laptop is definitely worth a look.

wongs at 2:29 AM No comments: Read More

Bringing brains to computers

For decades, scientists have fantasized about creating robots with brain-like intelligence. This year, researchers tempted by that dream made great progress on achieving what has been called the holy grail of computing.

Today, a wide variety of efforts are aimed at creating intelligent computers that can progressively learn and make smarter decisions. Millions of dollars this year were poured in efforts to create “silicon brains,” or neuromorphic chips that mimic brain-like functionality to make computers smarter.

The new chips could give eyes and ears to smart robots, which will be able to drive, identify objects, or even point out rotten fruit. This chip technology could let humans get mind control over machines, mobile devices anticipate actions by users and wearable devices like Google Glass to diagnose diseases. In the long run, neural chip implants could boost mental, visual, and cognitive capabilities of humans.

Scientists are looking to create advanced computers with these neural chips, which replicate the brain’s circuitry and can retain information and make decisions based on patterns discovered through probabilities and associations. Projects funded by the U.S. government, the European Union, and private organizations are attempting to re-create the manner in which the brain’s neurons and synapses work by redesigning the memory, computation, and communication features of traditional circuitry.

[Related: Biologically inspired--how neural networks are finally maturing]

The brain has 100 billion interconnected neurons, nerve cells that process and transmit information via electrical and chemical signals. These neurons compute in parallel and communicate via trillions of connections, which are the synapses. Connections among neurons in the neural network are either strengthened or pruned as the brain learns more. Today’s processors are wired and regulate voltage differently than the brain’s neural network, but researchers are keen on exploiting the parallelism of the brain which, among other things, also reduces power requirements.

Researchers hope neural chips will accomplish cognitive tasks and respond to a wide range of stimuli. Computers can already see and hear; robots have already been built to respond to sensory input. Within five years, computers could get smell and taste, and this sort of sensory information could be fed to chips for processing.

If we only had a brain (to simulate)

To be sure, most of the chip-development efforts are in early experimental phases. Brains of small insects and worms have been simulated on prototype neural chips, but human brains operate on a different scale. While it could be decades until chips simulate the human brain, the groundwork is bring laid by new models of computing that are now being established.

Among other things, new data-processing techniques are needed that allow more information to be fed to computers, researchers said. Helping this effort, the physical limits of manufacturing techniques for the chips that power today’s computers could fall within a decade, opening the door for new computing designs and chip architectures, said Robert Colwell, director of the microsystems technology office at DARPA (Defense Advanced Research Projects Agency), in a speech earlier this year.

Currently, computers don’t have the capacity to learn from past experiences. Instead, they rely on pre-programmed code to make decisions. On the other hand, brain cells do not require programming, are high tolerance, can regenerate, and can draw conclusions that computers are not able to reach, said Karlheinz Meier, professor and chair of experimental physics at the University of Heidelberg.

Traditional computers won’t go away, meanwhile, as some activities don’t require intelligent processing, said Meier, who is also co-director of the European Union-funded Human Brain Project.

“You will always do your text processing and email,” Meier said.

But like the brain, neural chips will excel at certain things, like cutting through “noisy” data to make intelligent decisions, said Nabil Imam, a computer scientist and researcher at Cornell University.

The neuromorphic chips will complement, not replace, other processors in a computer, Imam said.

Chips modeled after the human brain have electronic neurons that can dynamically rewire the connections among them, blast information at each other, and forage for relevant data—a process more power efficient than throwing lots of data to CPUs and other coprocessors like GPUs. IBM’s Watson supercomputer made history when it beat participants at the game of Jeopardy, but it threw lots of data at processors to find answers.

“Our brains were wired to do certain things very well like pattern recognition. Computers can’t do that. These processors have a different class of applications,” Imam said.

Imam is involved in the development of neuromorphic chips as part of the multiphase Synapse (Systems of Neuromorphic Adaptive Plastic Scalable Electronics) project funded by DARPA. The Synapse project, initiated in 2008, involves IBM, Hewlett-Packard, Cornell, Stanford University and other universities.

Neuromorphic chip from DARPA DARPA
Neuromorphic chip from DARPA

The first tangible results for Synapse came in early 2011, when IBM demonstrated a prototype chip with 256 digital neurons running at slow speeds of 10MHz. The chip was able to demonstrate navigation and pattern recognition abilities.

One chip core had 262,144 programmable synapses, while another core had 65,536 learning synapses. The connections between digital neurons got stronger depending on the number of signals sent. If an electronic spike from one neuron affects the voltage of another neuron, the two are synaptically connected. In chips, spiking neurons communicate with other neurons when triggers, such as certain values, are reached.

The next big thing

The next big Synapse announcement will come next year, when a new neural chip system that mimics a “very big brain” will be announced, Imam said. The chip will have a novel design of memory arrays so that large numbers of connections can be made among digital neurons. An asynchronous design will ensure communication signals are organized by local circuits. The chip will be made using a new manufacturing process.

“It’s the largest neuromorphic system that’s been built to date,” Imam said.

IBM, one of the lead researcher companies in Synapse, this year said that it ultimately wants to build a “chip system” that has 10 billion neurons and a hundred trillion synapses but draws just 1 kilowatt of power.

Another research project drawing interest is Qualcomm’s Zeroth chip, which the company calls a “neural processing unit.” By analyzing patterns of human behavior, the chip could make interaction with mobile devices easier by anticipating user actions, said company CEO Paul Jacobs during a speech last month.

Qualcomm has already demonstrated a robot based on Zeroth that can make navigation decisions. The company wants to expand Zeroth’s capabilities and is researching possibilities, said Sameer Kumar , director of business development at Qualcomm.

The Synapse and Qualcomm research efforts are based on digital neurons, but one neuromorphic system due in Europe will be based on analog circuitry, which keeps it truer to the brain. The system, located at the University of Heidelberg in Germany, is part of the Human Brain Project, a 10-year, $1.6 billion effort backed by the European Union to understand the brain’s inner workings.

The university already has a neuromorphic computing system operational with a silicon wafer containing 200,000 neurons and 50 million synapses. In two years, researchers hope to offer a 20-wafer system with a combined 4 million analog neurons, said Meier, who is spearheading the project.

The highly parallel chip design has configurable electronic neurons, and the goal is to understand the dependencies, synchronization and communication among neurons and synapses, and adopt them to computing.

The project’s intent is not to develop the best neural chip, but to understand architectures, Meier said. That could pave the way for neuromorphic computing models.

Other neural chip research efforts include Stanford’s Neurogrid and the University of Manchester’s Spinnaker, which is part of the E.U.’s Human Brain Project. HP is developing memristor memory technology, which could bolster a computer’s decision-making ability by understanding patterns from previously collected data, much like human brains collecting memories of and understanding a series of events.

It’s easy to create theories, but what’s important is to make the chips usable, said Guy Paillet, who holds a 1995 patent on neural circuit design, along with IBM and others. Paillet is the executive chairman of General Vision, which sells a chip called CM1K, based on a neural network design.

Research efforts under way are focused on so-called spiking neurons, which Paillet said are “close to biology to replicate the synapse model.”

Copying features of the way the brain works and applying them to chip technology is easier said than done. Neuron behavior is hard to predict, and making a chip that rewires millions of connections is a challenge. Morever, the brain is yet to be fully understood, and neuroscience researchers are uncovering new facts everyday.

But neural chip researchers are sharing data and taking complementary approaches, Meier said, adding that a little competition among peers doesn’t hurt.

“This is a chance to produce a new way of computing and we have to do whatever we can,” Meier said.

wongs at 1:36 AM No comments: Read More

computers networking science

Biologically inspired: How neural networks are finally maturing

More than two decades ago, neural networks were widely seen as the next generation of computing, one that would finally allow computers to think for themselves.

Now, the ideas around the technology, loosely based on the biological knowledge of how the mammalian brain learns, are finally starting to seep into mainstream computing, thanks to improvements in hardware and refinements in software models.

Computers still can’t think for themselves, of course, but the latest innovations in neural networks allow computers to sift through vast realms of data and draw basic conclusions without the help of human operators.

“Neural networks allow you to solve problems you don’t know how to solve,” said Leon Reznik, a professor of computer science at the Rochester Institute of Technology.

Slowly, neural networks are seeping into industry as well. Micron and IBM are building hardware that can be used to create more advanced neural networks.

On the software side, neural networks are slowly moving into production settings as well. Google has applied various neural network algorithms to improve its voice recognition application, Google Voice. For mobile devices, Google Voice translates human voice input to text, allowing users to dictate short messages, voice search queries and user commands even in the kind of noisy ambient conditions that would flummox traditional voice recognition software.

Neural networks could also be used to analyze vast amounts of data. In 2009, a group of researchers used neural network techniques to win the Netflix Grand Prize.

At the time, Netflix was holding a yearly contest to find the best way to recommend new movies based on its data set of approximately 100 million movie ratings from its users. The challenge was to come up with a better way to recommend new movie choices to users than Netflix’s own recommendation system. The winning entry was able to improve on Netflix’s internal software, offering a more accurate predictor of what movies Netflix may want to see.

Neural networking vs. computing
As originally conceived, neural networking differs from traditional computing in that, with conventional computing, the computer is given a specific algorithm, or program, to execute. With neural networking, the job of solving a specific problem is largely left in the hands of the computer itself, Reznick said.

To solve a problem such as finding a specific object against a backdrop, neural networks use a similar, though vastly simplified, approach to how a mammalian cerebral cortex operates. The brain processes sensory and other information using billions of interconnected neurons. Over time, the connections among the neurons change, by growing stronger or weaker in a feedback loop, as the person learns more about his or her environment.

An artificial neural network (ANN) also uses this approach of modifying the strength of connections among different layers of neurons, or nodes in the parlance of the ANN. ANNs, however, usually deploy a training algorithm of some form, which adjusts the nodes to extract the desired features from the source data. Much like humans do, a neural network can generalize, slowly building up the ability to recognize, for instance, different types of dogs, using a single image of a dog.

There are numerous efforts under way to try to replicate, at high fidelity, how the brain operates in hardware, such as the EU’s Human Brain Project (see accompanying story: “Bringing brains to computers”). Researchers in the field of computer science, however, are borrowing the ideas from biology to build systems that, over time, may learn in the same way brains do, even if their approach differs from that of biological organisms.

Evolution of neural networking
Although investigated since the 1940s, research into ANNs, which can be thought of as a form of artificial intelligence (AI), hit a peak of popularity in the late 1980s.

“There was a lot of great things done as part of the neural network resurgence in the late 1980s,” said Dharmendra Modha, an IBM Research senior manager who is involved in a company project to build a neuromorphic processor. Throughout the next decade, however, other forms of closely related AI started getting more attention, such as machine learning and expert systems, thanks to a more immediate applicability to industry usage.

Nonetheless, the state-of-the-art in neural networks continued to evolve, with the introduction of powerful new learning models that could be layered to sharpen performance in pattern recognition and other capabilities,

“We’ve come to the stage where much closer simulation of natural neural networks is possible with artificial means,” Reznick said. While we still don’t know entirely how the brain works, a lot of advances have been made in cognitive science, which, in turn, are influencing the models that computer scientists are using to build neural networks.

“That means that now our artificial computer models will be much closer to the way natural neural networks process information,” Reznick said.

The continuing march of Moore’s Law has also lent a helping hand. Over the past decade, the microprocessor fabrication process has provided the density needed to run large clusters of nodes even on a single slice of silicon, a density that would not have been possible even a decade ago.

“We’re now at a point where the silicon has matured and technology nodes have gotten dense enough where it can deliver unbelievable scale at really low power,” Modha said.

Harnessing processors
Reznick is leading a number of projects to harness today’s processors in a neural network-like fashion. He is investigating the possibility of using GPUs (graphics processing units), which thanks to their large number of processing cores, are inherently adapt at parallel computing. He is also investigating how neural networking could improve intrusion detection systems, which are used for detect everything from trespassers on a property to malicious hackers trying to break into a computer system.

Today’s intrusion detection systems work in one of two ways, Reznick explained. They either use signature detection, in which they recognize a pattern based on a pre-existing library of patterns. Or they look for anomalies in a typically static backdrop, which can be difficult to do in scenarios with lots of activity. Neural networking could combine the two approaches to strengthen the ability of the system to detect unusual deviations from the norm, Reznick said

One hardware company investigating the possibilities of neural networking is Micron. The company has just released a prototype of a DDR memory module with a built-in processor, called Automata.

While not a replacement for standard CPUs, a set of Automata modules could be used to watch over a live stream of incoming data, seeking anomalies or patterns of interest. In addition to these spatial characteristics, they can also watch for changes over time, said Paul Dlugosch, director of Automata processor development in the architecture development group of Micron’s DRAM division.

“We were in some ways biologically inspired, but we made no attempt to achieve a high fidelity model of a neuron. We were focused on a practical implementation in a semiconductor device, and that dictated many of our design decisions,” Dlugosch said.

Nonetheless, because they can be run in parallel, multiple Automata modules, each serving as a node, could be run together in a cluster for doing neural network-like computations. The output of one module can be piped into another module, providing the multiple layers of nodes needed for neural networking. Programming the Automata can be done through a compiler that Micron developed that uses either an extension of the regular expression language or its own Automata Network Markup Language (ANML).

Another company investigating this area is IBM. In 2013, IBM announced it had developed a programming model for some cognitive processors it built as part of the U.S. Defense Advanced Research Projects Agency (DARPA) SyNAPSE (Systems of Neuromorphic Adaptive Plastic Scalable Electronics) program.

IBM’s programming model for these processors is based on reusable and stackable building blocks, called corelets. Each corelet is in fact a tiny neural network itself and can be combined with other corelets to build functionality. “One can compose complex algorithms and applications by combining boxes hierarchically,” Modha said.

“A corelet equals a core. You expose the 256 wires emerging out of the neurons, and expose 256 axioms going into the core but inside of the code is not exposed. From the outside perspective, you only see these wires,” Modha said.

In early tests, IBM taught one chip how to play the primitive computer game Pong, to recognize digits, to do some olfactory processing, and to navigate a robot through a simple environment.

While it is doubtful that neural networks would ever replace standard CPUs, they may very well end up tackling certain types of jobs difficult for CPUs alone to handle.

“Instead of bringing sensory data to computation, we are bringing computation to sensors,” Modha said. “This is not trying to replace computers, but it is a complementary paradigm to further enhance civilization’s capability for automation.”

wongs at 1:29 AM No comments: Read More

Wong's Gathot

Recent Post