Cue the sad tuba and attorney jokes: Machines just landed the hurt on lawyers. LawGeex, an Israel-based startup focused on automating contract reviews, released a study showing its AI software pummels lawyers in document review accuracy. The AI service outperformed 20 corporate lawyers at identifying legal risks in nondisclosure agreement contracts. But don't worry, the machines got no papercuts. Undisclosed, however, is whether the lawyers involved in the study have sent their billable hours invoices to the machines for payment.

Amazon has admitted that its Echo devices are emitting horrifying, creepy laughs. The company has promised to fix the issue with Alexa that is causing the chilling noises to come out of it. But it's not before the problem has caused many people not to be able to get to sleep – and led some to completely unplug their devices out of fear. Towards the end of February, some Echo owners reported that their devices appeared to start laughing for no reason. The noises would simply come out of the little cylinder, despite not being requested.

For a field that was not well known outside of academia a decade ago, artificial intelligence has grown dizzyingly fast. Tech companies from Silicon Valley to Beijing are betting everything on it, venture capitalists are pouring billions into research and development, and start-ups are being created on what seems like a daily basis. If our era is the next Industrial Revolution, as many claim, A.I. is surely one of its driving forces. It is an especially exciting time for a researcher like me. When I was a graduate student in computer science in the early 2000s, computers were barely able to detect sharp edges in photographs, let alone recognize something as loosely defined as a human face.

Apple has revealed new details about how exactly the iPhone is made – and how it is trying to improve the lives of people who are actually putting it together. The new Supplier Responsibility report for 2017 shows just how potentially damaging that process is. But it reveals how at least some of those problems can be removed from it, and what the company is doing to make sure it is taking those steps. The problem facing Apple and other technology companies is easy to explain, but incredibly difficult and complicated to solve. Those companies are mostly sincere in their desire to ensure people aren't hurt while making the phones in our pockets and the computers on our desks, and are largely held responsible when they fail to do so – but the suppliers are operating separately from companies like Apple and often in places with fewer worker protections and more abuses.

Samsung Electronics is now believed to be upgrading its Bixby artificial intelligence platform following its latest acquisition. The South Korean giant's affiliate has apparently acquired AI search engine startup Kngine. The Investor learned Wednesday that Samsung Research America has acquired a 100-percent stake in Kngine, a startup that develops mobile solutions that understand and answer inquiries with the use of AI including deep learning. The functionality is something that could strengthen Bixby as a whole and set it apart from its rivals, like Siri, Alexa and Google Assistant. Kngine started its operations in 2013 in Egypt.

Collaborative Innovation Center of Quantum Matter, Beijing, 100084, China (Dated: March 12, 2018) Motivated by the question that whether the empirical fitting of data by neural networks can yield the same structure of physical laws, we apply neural networks to a quantum mechanical two-body scattering problem with short-range potentials--a problem by itself plays an important role in many branches of physics. After training, the neural network can accurately predict s - wave scattering length, which governs the low-energy scattering physics. By visualizing the neural network, we show that it develops perturbation theory order by order when the potential depth increases, without solving the Schr odinger equation or obtaining the wavefunction explicitly. The result provides an important benchmark to the machine-assisted physics research or even automated machine learning physics laws. Human physicists have made great achievements in discovering laws of physics during the last several centuries.

In extreme classification problems, learning algorithms are required to map instances to labels from an extremely large label set. We build on a recent extreme classification framework with logarithmic time and space, and on a general approach for error correcting output coding (ECOC), and introduce a flexible and efficient approach accompanied by bounds. Our framework employs output codes induced by graphs, and offers a tradeoff between accuracy and model size. We show how to find the sweet spot of this tradeoff using only the training data. Our experimental study demonstrates the validity of our assumptions and claims, and shows the superiority of our method compared with state-of-the-art algorithms.

The continuous dynamical system approach to deep learning is explored in order to devise alternative frameworks for training algorithms. Training is recast as a control problem and this allows us to formulate necessary optimality conditions in continuous time using the Pontryagin's maximum principle (PMP). A modification of the method of successive approximations is then used to solve the PMP, giving rise to an alternative training algorithm for deep learning. This approach has the advantage that rigorous error estimates and convergence results can be established. We also show that it may avoid some pitfalls of gradient-based methods, such as slow convergence on flat landscapes near saddle points. Furthermore, we demonstrate that it obtains favorable initial convergence rate per-iteration, provided Hamiltonian maximization can be efficiently carried out - a step which is still in need of improvement. Overall, the approach opens up new avenues to attack problems associated with deep learning, such as trapping in slow manifolds and inapplicability of gradient-based methods for discrete trainable variables.

We present a new approach for mitigating unfairness in learned classifiers. In particular, we focus on binary classification tasks over individuals from two populations, where, as our criterion for fairness, we wish to achieve similar false positive rates in both populations, and similar false negative rates in both populations. As a proof of concept, we implement our approach and empirically evaluate its ability to achieve both fairness and accuracy, using datasets from the fields of criminal risk assessment, credit, lending, and college admissions.

We consider high-dimensional binary classification by sparse logistic regression. We propose a model/feature selection procedure based on penalized maximum likelihood with a complexity penalty on the model size and derive the non-asymptotic bounds for the resulting misclassification excess risk. The bounds can be reduced under the additional low-noise condition. The proposed complexity penalty is remarkably related to the VC-dimension of a set of sparse linear classifiers. Implementation of any complexity penalty-based criterion, however, requires a combinatorial search over all possible models. To find a model selection procedure computationally feasible for high-dimensional data, we extend the Slope estimator for logistic regression and show that under an additional weighted restricted eigenvalue condition it is rate-optimal in the minimax sense.