A major target of linguistics and cognitive science has been to understand what class of learning systems can acquire the key structures of natural language. Until recently, the computational requirements of language have been used to argue that learning is impossible without a highly constrained hypothesis space. Here, we describe a learning system that is maximally unconstrained, operating over the space of all computations, and is able to acquire several of the key structures present natural language from positive evidence alone. The model successfully acquires regular (e.g. $(ab)^n$), context-free (e.g. $a^n b^n$, $x x^R$), and context-sensitive (e.g. $a^nb^nc^n$, $a^nb^mc^nd^m$, $xx$) formal languages. Our approach develops the concept of factorized programs in Bayesian program induction in order to help manage the complexity of representation. We show in learning, the model predicts several phenomena empirically observed in human grammar acquisition experiments.

Sensor data has been playing an important role in machine learning tasks, complementary to the human-annotated data that is usually rather costly. However, due to systematic or accidental mis-operations, sensor data comes very often with a variety of missing values, resulting in considerable difficulties in the follow-up analysis and visualization. Previous work imputes the missing values by interpolating in the observational feature space, without consulting any latent (hidden) dynamics. In contrast, our model captures the latent complex temporal dynamics by summarizing each observation's context with a novel Iterative Imputing Network, thus significantly outperforms previous work on the benchmark Beijing air quality and meteorological dataset. Our model also yields consistent superiority over other methods in cases of different missing rates.

Non-negative matrix factorization (NMF) is a technique for finding latent representations of data. The method has been applied to corpora to construct topic models. However, NMF has likelihood assumptions which are often violated by real document corpora. We present a double parametric bootstrap test for evaluating the fit of an NMF-based topic model based on the duality of the KL divergence and Poisson maximum likelihood estimation. The test correctly identifies whether a topic model based on an NMF approach yields reliable results in simulated and real data.

Gurgaon-based mentorship-driven incubator India Accelerator will make seed investment worth around $150,000 in six startups that operate across sectors such as fin-tech, artificial intelligence, transport and e-commerce. Each of the startups, which are selected from a list of close to 150 early-stage firms, will receive $20,000 to $25,000, India Accelerator said in a statement. "While many players in the industry provide funding as the primary offering, we would like to create a more meaningful difference. Post the seed funding, India Accelerator will handhold these startups through the intensive three-month mentorship-driven programme, said Mona Singh, chief acceleration officer of India Accelerator. Founded by Ashish Bhatia and Abhay Chawla, earlier this year, India Accelerator is a member of Global Accelerator Network, a consortium of more than 85 accelerators around the globe. Voko: It is an online rental platform that provides a convenient and affordable way to get consumer goods on rent.

GURUGRAM: Maintaining a positive outlook towards the advent of new technologies in the IT industry, National Association of Software and Services Companies (Nasscom) president R Chandrashekhar said automation should be looked on as an opportunity and not as a challenge. Speaking at Nasscom's annual technology conference held in Gurgaon on Friday, Chandrashekhar said, "We've real-life examples of how digitisation and automation pose an opportunity and not a challenge for enterprises in India. Artificial intelligence and analytics can transform every aspect of business, from algorithm economy to intelligent talent management." Multiple organisations participated in the event and brainstormed around the theme'prepare to disrupt'. Chandrashekhar has repeatedly rubbished the claims of Indian IT industry losing jobs to technology.

Huachangda Intelligent Equipment, a Chinese industrial robot integrator primarily servicing China's auto industry, has acquired Swedish Robot System Products (RSP), a 2003 spin-off from ABB with 70 employees in Sweden, Germany and China, for an undisclosed amount. RSP manufactures grippers, welding equipment, tool changers and other peripheral products for robots. Last month HTI Cybernetics, a Michigan industrial robotics integrator and contract manufacturer, was acquired by Chongqing Nanshang Investment Group for around $50 million. HTI provides robotic welding systems to the auto industry and also has a contract welding services facility in Mexico. China is in the midst of a national program to develop or acquire its own technology to rival similar technologies in the West, particularly in futuristic industries such as robotics, electric cars, self-driving vehicles and artificial intelligence.

The UK doesn't want to sit by the wayside while the US, Japan and other countries streamline the adoption of self-driving cars. The country's finance ministry has revealed that its upcoming budget (due on November 22nd) will include measures intended to spurt the adoption of self-driving and electric cars. There will be rule changes that let automakers test vehicles on public roads without an operator on standby, and a £400 million (about $529 million) fund to help companies establish charging station networks. Officials will also offer £100 million ($132 million) in incentives to lower the cost of buying an EV. There are a few other tech-related budget measures, such as £160 million ($211 million) for 5G networks, £100 million for computer science teachers, £76 million ($100 million) for skill development and £75 million ($99 million) for the UK's budding AI industry.

How do you find errors in a system that exists in a black box whose contents are a mystery even to experts? That is one of the challenges of perfecting self-driving cars and other deep learning systems that are based on artificial neural networks--known as deep neural networks--modeled after the human brain. Inside these systems, a web of neurons enables a machine to process data with a nonlinear approach and, essentially, to teach itself to analyze information through what is known as training data. When an input is presented to a "trained" system--like an image of a typical two-lane highway shown to a self-driving car platform--the system recognizes it by running an analysis through its complex logic system. This process largely occurs inside a black box and is not fully understood by anyone, including a system's creators. Any errors also occur inside the black box and are thus difficult to identify and fix.

Across the world, higher education is increasingly being judged through the lens of employability. More and more, politicians are asking universities how they are preparing students for work, and even tying their funding to their graduates' success in the workplace. In the West, this has mainly been a result of the squeeze on the public purse and – in some countries, at least – an accompanying rise in tuition fees. But there is also growing anxiety about the technological revolution's potential to replace large numbers of human workers with computers and robots if humans can't keep one step ahead in the race to acquire skills. So how well are universities meeting the challenge of preparing graduates for the digital age?

Satya Nadella spoke at India Today Conclave 2017 where he talked about the spread of digital technology in the Indian landscape. Nadella shared his experience of how India changed from being a service provider to a using its own IT prowess in the various sectors. During his session at the Conclave, Nadella shared a few instances where artificial intelligence and mixed reality have already started making major impacts in the fields of health and business development. Nadella spoke about the numerous opportunities that come with technological advancements. However, he believes that with tremendous opportunity comes tremendous responsibility.