Both of the major smart home platforms have a long-running problem with "discoverability": it's very hard to let users know what their devices can do, particularly if they're always improving thanks to rapid software updates. Amazon and Google are constantly experimenting with ways to get around this, but so far they have been timid. Amazon sends a weekly email, while Google includes some tips in its app. Expect to see them be bolder, particularly as powerful rivals such as Apple appear on the scene with worse AI but better sound. So don't be surprised if your Google Home or Amazon Echo begin to talk back, rather than simply following commands.

LG is releasing a new line of premium audio devices in 2018. Ahead of the products' release, the South Korea giant has decided to introduce them via its online newsroom. Among the home speakers that LG is launching are the ThinQ Speaker and the SK10Y soundbar. LG's first premium audio device for 2018 is the ThinQ Speaker which features Google Assistant. Aside from giving users access to a digital assistant, the audio product is also built to offer high-quality sound experience.

After becoming the first robot to get citizenship, this was Sophia's maiden India visit (PTI)

Last year we showed you Kengoro, a Japanese robot that exercises and even sweats. Well, now the machine is back in the news and he brought a friend named Kenshiro. The pair like to get buff, compare muscle mass, high five each other, and say "bro" a lot, before filming Rocky-style montages of more workouts. You're right, I made a lot of that up, but once the technology exists, that stuff will happen too. These two humanoid robots come from a team of Japanese researchers from the Graduate School of Information Science and Technology at The University of Tokyo.

The insecurity of employment is much higher these days because of a high level of competition. Outsourcing is not restricted to humans anymore. Instead of worrying about other people stealing your job, you may now be anxious that you might lose it to automation. In simpler words, you should be worried that robots may take away your job. Now, robots not only mean machines that look like humans (the ones we typically see in movies).

Data from your smartphone sensors can reveal PINs and passwords to hackers and allow them to unlock your mobile devices, according to a study led by an Indian-origin scientist. Instruments in smartphones such as the gyroscope and proximity sensors represent a potential security vulnerability, researchers from Nanyang Technological University (NTU) in Singapore said. Using machine learning algorithms and a combination of information gathered from six different sensors found in smartphones, researchers succeeded in unlocking Android smartphones with a 99.5 per cent accuracy within only three tries, when tackling a phone that had one of the 50 most common PIN numbers. The previous best phone-cracking success rate was 74 per cent for the 50 most common PIN numbers, but NTU's technique can be used to guess all 10,000 possible combinations of four-digit PINs. Led by Shivam Bhasin, NTU Senior Research Scientist, researchers used sensors in a smartphone to model which number had been pressed by its users, based on how the phone was tilted and how much light is blocked by the thumb or fingers.

At a high level, Artificial Intelligence (AI) is a branch of computer science that makes machines imitate intelligent human behavior, simulating (and often exceeding) human performance. AI has finally emerged as the future, after unfulfilled hype that goes back to the 1950s, due to developments such as the availability of an immense amount of data, the open-sourcing of ML algorithm development, and advances in high-density parallel processing infrastructure. In fact, IBM now believes the technology solutions market for AI amounts to a staggering $2 trillion over the next decade. Data is the new oil, and 90% of data in the world right now has been created in the last 2 years alone. The power of data has actually lagged the technical capability to monetize it efficiently and effectively, in a world where the use of data is moving from a competitive advantage to a requirement to compete.

Computing partition function is the most important statistical inference task arising in applications of Graphical Models (GM). Since it is computationally intractable, approximate methods have been used in practice, where mean-field (MF) and belief propagation (BP) are arguably the most popular and successful approaches of a variational type. In this paper, we propose two new variational schemes, coined Gauged-MF (G-MF) and Gauged-BP (G-BP), improving MF and BP, respectively. Both provide lower bounds for the partition function by utilizing the so-called gauge transformation which modifies factors of GM while keeping the partition function invariant. Moreover, we prove that both G-MF and G-BP are exact for GMs with a single loop of a special structure, even though the bare MF and BP perform badly in this case. Our extensive experiments indeed confirm that the proposed algorithms outperform and generalize MF and BP.

Estimation in generalized linear models (GLM) is complicated by the presence of constraints. One can handle constraints by maximizing a penalized log-likelihood. Penalties such as the lasso are effective in high dimensions but often lead to severe shrinkage. This paper explores instead penalizing the squared distance to constraint sets. Distance penalties are more flexible than algebraic and regularization penalties, and avoid the drawback of shrinkage. To optimize distance penalized objectives, we make use of the majorization-minimization principle. Resulting algorithms constructed within this framework are amenable to acceleration and come with global convergence guarantees. Applications to shape constraints, sparse regression, and rank-restricted matrix regression on synthetic and real data showcase the strong empirical performance of distance penalization, even under non-convex constraints.

It is well known that, for a linear program (LP) with constraint matrix $\mathbf{A}\in\mathbb{R}^{m\times n}$, the Alternating Direction Method of Multiplier converges globally and linearly at a rate $O((\|\mathbf{A}\|_F^2+mn)\log(1/\epsilon))$. However, such a rate is related to the problem dimension and the algorithm exhibits a slow and fluctuating ``tail convergence'' in practice. In this paper, we propose a new variable splitting method of LP and prove that our method has a convergence rate of $O(\|\mathbf{A}\|^2\log(1/\epsilon))$. The proof is based on simultaneously estimating the distance from a pair of primal dual iterates to the optimal primal and dual solution set by certain residuals. In practice, we result in a new first-order LP solver that can exploit both the sparsity and the specific structure of matrix $\mathbf{A}$ and a significant speedup for important problems such as basis pursuit, inverse covariance matrix estimation, L1 SVM and nonnegative matrix factorization problem compared with current fastest LP solvers.