I met Mr. Lee in Taiping, China, circa 1993. He managed "Warehouse B," a massive structure that stored over 3,000 reusable tools and dies. A humble man, he sat quietly at his high-top desk in a simple button-down shirt and black slacks. His workers treated him with the respect you might reserve for a beloved grandfather, each literally running to do his bidding before he would even finish a gently delivered request. His ledgers were meticulously neat.

I never learned anything in the Saturday-morning Chinese school I was forced to attend as a child, but that's not what motivates my choice here. There were plenty of reasons for my poor performance in those classes--my resentment at having to miss the "Super Friends" cartoon being just one of them--so I don't blame Chinese characters for my failure. No, my objection is a practical one: I'm a fan of literacy, and Chinese characters have been an obstacle to literacy for millennia. With a phonetic writing system like an alphabet or a syllabary, you need only learn a few dozen symbols and you can read most everything printed in a newspaper. With Chinese characters, you have to learn three thousand.

On a bitter, soul-shivering, damp, biting gray February day in Cleveland--that is to say, on a February day in Cleveland--a handless man is handling a nonexistent ball. Igor Spetic lost his right hand when his forearm was pulped in an industrial accident six years ago and had to be amputated. In an operation four years ago, a team of surgeons implanted a set of small translucent "interfaces" into the neural circuits of his upper arm. This afternoon, in a basement lab at a Veterans Administration hospital, the wires are hooked up directly to a prosthetic hand--plastic, flesh-colored, five-fingered, and articulated--that is affixed to what remains of his arm. The hand has more than a dozen pressure sensors within it, and their signals can be transformed by a computer into electric waves like those natural to the nervous system. Since, from the brain's point of view, his hand is still there, it needs only to be recalled to life. With the "stimulation" turned on--the electronic feed coursing from the sensors--Spetic feels nineteen distinct sensations in his artificial hand. Above all, he can feel pressure as he would with a living hand. "We don't appreciate how much of our behavior is governed by our intense sensitivity to pressure," Dustin Tyler, the fresh-faced principal investigator on the Cleveland project, says, observing Spetic closely. "We think of hot and cold, or of textures, silk and cotton. But some of the most important sensing we do with our fingers is to register incredibly minute differences in pressure, of the kinds that are necessary to perform tasks, which we grasp in a microsecond from the feel of the outer shell of the thing. We know instantly, just by touching, whether to gently squeeze the toothpaste or crush the can." With the new prosthesis, Spetic can sense the surface of a cherry in a way that allows him to stem it effortlessly and precisely, guided by what he feels, rather than by what he sees. Prosthetic hands like Spetic's tend to be super-strong, capable of forty pounds of pressure, so the risk of crushing an egg is real. The stimulation sensors make delicate tasks easy. Spetic comes into the lab every other week; the rest of the time he is busy pursuing a degree in engineering, which he has taken up while on disability.

IBM will launch a Korean version of its AI platform Watson next year in cooperation with local IT service vendor SK C&C, the companies have announced. SK announced Monday that it signed a cooperation agreement with Big Blue on May 4 and will together build an integrated system to market Watson in South Korea. They will develop Korean data analysis solutions based on machine learning and natural language semantic analysis technology for Watson within this year, and will commercialise it sometime in the first half of 2017, SK said. IBM and SK will also build a "Watson Cloud Platform" at the Korean company's datacentre in Pangyo -- the local version of Silicon Valley -- that IT developers and managers can access to make their own applications. For example, an open market business can apply the Watson solution to its product search features to make a personalized contents recommendation solution.

Back when activity based working (ABW) was starting to gain real traction in places like Australia in 2013, we ran some research on which companies were most influential on the organisational leaders that were driving adoption of the work style. For many technology vendors and service providers the results from our more than 50 in-depth interviews with ABW adopters (now more than 250) were somewhat of a shock. The leading influencers were: #1 workpace strategy specialists; #2 interior designers; #3 peers that had adopted ABW; #4 real estate management firms and furniture providers. On the contrary, it was considered critical to get right and pretty much underpinned everything (see our checklist for guidance here). But technology providers simply didn't have a vision or narrative that was resonating with organisational leaders that also had to consider how to best use physical space while changing cultures.

Efficient and accurate low-rank approximations of multiple data sources are essential in the era of big data. The scaling of kernel-based learning algorithms to large datasets is limited by the O(n^2) computation and storage complexity of the full kernel matrix, which is required by most of the recent kernel learning algorithms. We present the Mklaren algorithm to approximate multiple kernel matrices learn a regression model, which is entirely based on geometrical concepts. The algorithm does not require access to full kernel matrices yet it accounts for the correlations between all kernels. It uses Incomplete Cholesky decomposition, where pivot selection is based on least-angle regression in the combined, low-dimensional feature space. The algorithm has linear complexity in the number of data points and kernels. When explicit feature space induced by the kernel can be constructed, a mapping from the dual to the primal Ridge regression weights is used for model interpretation. The Mklaren algorithm was tested on eight standard regression datasets. It outperforms contemporary kernel matrix approximation approaches when learning with multiple kernels. It identifies relevant kernels, achieving highest explained variance than other multiple kernel learning methods for the same number of iterations. Test accuracy, equivalent to the one using full kernel matrices, was achieved with at significantly lower approximation ranks. A difference in run times of two orders of magnitude was observed when either the number of samples or kernels exceeds 3000.

The refugee crisis is perhaps the single most challenging problem for Europe today. Hundreds of thousands of people have already traveled across dangerous sea passages from Turkish shores to Greek islands, resulting in thousands of dead and missing, despite the best rescue efforts from both sides. One of the main reasons is the total lack of any early warning-alerting system, which could provide some preparation time for the prompt and effective deployment of resources at the hot zones. This work is such an attempt for a systemic analysis of the refugee influx in Greece, aiming at (a) the statistical and signal-level characterization of the smuggling networks and (b) the formulation and preliminary assessment of such models for predictive purposes, i.e., as the basis of such an early warning-alerting protocol. To our knowledge, this is the first-ever attempt to design such a system, since this refugee crisis itself and its geographical properties are unique (intense event handling, little or no warning). The analysis employs a wide range of statistical, signal-based and matrix factorization (decomposition) techniques, including linear & linear-cosine regression, spectral analysis, ARMA, SVD, Probabilistic PCA, ICA, K-SVD for Dictionary Learning, as well as fractal dimension analysis. It is established that the behavioral patterns of the smuggling networks closely match (as expected) the regular burst and pause periods of store-and-forward networks in digital communications. There are also major periodic trends in the range of 6.2-6.5 days and strong correlations in lags of four or more days, with distinct preference in the Sunday-Monday 48-hour time frame. These results show that such models can be used successfully for short-term forecasting of the influx intensity, producing an invaluable operational asset for planners, decision-makers and first-responders.

We revisit the problem of inferring the overall ranking among entities in the framework of Bradley-Terry-Luce (BTL) model, based on available empirical data on pairwise preferences. By a simple transformation, we can cast the problem as that of solving a noisy linear system, for which a ready algorithm is available in the form of the randomized Kaczmarz method. This scheme is provably convergent, has excellent empirical performance, and is amenable to on-line, distributed and asynchronous variants. Convergence, convergence rate, and error analysis of the proposed algorithm are presented and several numerical experiments are conducted whose results validate our theoretical findings.

The first commercial spam message was sent in 1994--at least that's the general consensus. Lawrence Canter and Margaret Siegel had a program written that would post a copy of an advertisement for their law firm's green card lottery paperwork service to every Usenet news group -- about 6,000 of them. Because of the way the messages were posted, Usenet clients couldn't filter out duplicate copies, and users saw a copy of the same message in every group. At the time, commercial use of internet resources was rare (it had only recently become legal) and access to Usenet was expensive. Users considered these commercial-seeming messages to be crass--not only did they take up their time, but they also cost them money.

I first met Pepper in 2014, a human-shaped robot at a mobile store of Akihabara district in Tokyo. Although our conversation quickly reached some limits by the fact that he (it?) could only speak Japanese at the time, I sympathized with what his creator Aldebaran (a French company now part of the Softbank Japanese conglomerate) defines as a genuine day-to-day companion, whose number one quality is his ability to perceive emotions and adjust his behavior to your mood based on your voice, face expression and words you use. To-date, 10,000 Pepper robots have been sold mostly to Japanese homes. One third of them are used as an attraction to surprise customers and inform them. Nestlé is planning on equipping more than 1,000 Nescafé sales outlets in Japan.