SILICON has been making our computers work for almost half a century. Whether designed for graphics or number crunching, all information processing is done using a million-strong horde of tiny logic gates made from element number 14. But silicon's time may soon be up. Moore's law – the prophecy which dictates that the number of silicon transistors on microprocessors doubles every two years – is grinding to a halt because there is a limit to how many can be squeezed on a chip. The machine-learning boom is another problem. The amount of energy silicon-based computers use is set to soar as they crunch more of the massive data sets that algorithms in this field require.

Do you know that two thirds of Chinese outbound travelers prefer individual travel? Data from the China National Tourism Administration (CNTA) shows that Chinese outbound travelers reached 120 million in 2014, among whom 80 million chose self-guided tours. While we can appreciate the various options available in transportation, accommodation, and attractions, it can also become perplexing and even frustrating to dig through a sea of information just for a travel plan. "The abundance of choice allows everyone to personalize a journey, yet filtering out useless and repeated information can be time-consuming and not cost-effective," said Zhang Fan, founder and CEO of the itinerary planner Mioji. Moji means "brilliant idea" in Chinese.

In our quarterly analysis of companies pursuing healthcare-focused applications of AI, we reported that deals leapt from less than 10 in 2011 to 60 in 2015. So far this year (as 0f 8/23/2016), companies in this space have raised around 55 equity funding rounds. Some of the recent deals include a 25M Series A round raised by London-based health services startup, Babylon Health, backed by investors including Kinnevik and Google-owned DeepMind Technologies (Babylon will reportedly roll out a Siri-like voice recognition interface this year), and a 154M Series A round raised by China-based iCarbonX. "By 2025, AI systems could be involved in everything from population health management, to digital avatars capable of answering specific patient queries." We identified over 80 companies that are applying machine learning algorithms and predictive analytics to reduce drug discovery times, provide virtual assistance to patients, and diagnose ailments by processing medical images, among other things.

NAIROBI – Japanese and African leaders on Sunday pledged to fight terrorism and emphasized the importance of rules-based maritime order as they wrapped up a Japan-led international conference on the continent's development. In the Nairobi Declaration adopted at the Tokyo International Conference on African Development (TICAD), the leaders also agreed to promote investment in infrastructure that leads to job creation in the fast-growing region. "Japan's public and private sectors will offer cooperation for the development that is led by Africa itself," Prime Minister Shinzo Abe told a news conference after wrap-up of the sixth TICAD, convened in the Kenyan capital of Nairobi. Kenyan President Uhuru Kenyatta told the same news conference that Japan does not press its own views on the continent and continues to be a force for African development. The triennial conference was held outside Japan for the first time, as Tokyo seeks to strengthen its economic and political presence in the continent amid China's increasing influence.

Seated against a deep red backdrop, gazing intently at hand-held mirrors, two eunuchs in sparkling saris inspect their appearance before Raksha Bandhan celebrations in the red light district of Mumbai. The photograph from the Reuters news agency is an arresting contemporary scene, but a new Tate Britain project is aiming to inspire deeper reflections with images from its own collection of paintings. Launching on Friday, Recognition is the winner of 2016's IK prize – an annual award, this year supported by Microsoft, for a project that embraces digital technology to explore and showcase Tate's collection of British art. This year, the challenge was to do it with artificial intelligence. The team behind the winning project, from the Italy-based communication research centre Fabrica, say their inspiration came from an intriguing conundrum: how can you apply rational thinking to a subject like art? Recognition matches stunning photographs from the 24/7 news cycle with centuries-old artworks, and presents them online.

This blog series is based on my upcoming talk on re-usability of Deep Learning Models at the Hadoop Strata World Conference in Singapore. This blog series will be in several parts – where I describe my experiences and go deep into the reasons behind my choices. Deep learning is an emerging field of research, which has its application across multiple domains. I try to show how transfer learning and fine tuning strategy leads to re-usability of the same Convolution Neural Network model in different disjoint domains. Application of this model across various different domains brings value to using this fine-tuned model.

Teacher's Day Special: Helping kids with special needs to shine

Clustering high-dimensional data often requires some form of dimensionality reduction, where clustered variables are separated from "noise-looking" variables. We cast this problem as finding a low-dimensional projection of the data which is well-clustered. This yields a one-dimensional projection in the simplest situation with two clusters, and extends naturally to a multi-label scenario for more than two clusters. In this paper, (a) we first show that this joint clustering and dimension reduction formulation is equivalent to previously proposed discriminative clustering frameworks, thus leading to convex relaxations of the problem, (b) we propose a novel sparse extension, which is still cast as a convex relaxation and allows estimation in higher dimensions, (c) we propose a natural extension for the multi-label scenario, (d) we provide a new theoretical analysis of the performance of these formulations with a simple probabilistic model, leading to scalings over the form $d=O(\sqrt{n})$ for the affine invariant case and $d=O(n)$ for the sparse case, where $n$ is the number of examples and $d$ the ambient dimension, and finally, (e) we propose an efficient iterative algorithm with running-time complexity proportional to $O(nd^2)$, improving on earlier algorithms which had quadratic complexity in the number of examples.

For the casual internet user, a quick Google search is often all it takes to find plenty of information on any particular topic. But for specialized financial research, analysts often find themselves laboriously searching proprietary databases, regulatory filings, and paywalled sources that aren't even indexed by the big search engines, says Jack Kokko, the founder and CEO of financial search engine company AlphaSense. That's why he and cofounder and CTO Raj Neervannan, created AlphaSense, which applies natural language processing and machine learning techniques to let users find relevant information in financial documents. "It started from my first job out of college as an analyst at Morgan Stanley, where I was, as every analyst, going through these huge piles of paper on my desk and trying to find information very manually--nights and days spent toiling through that information and still fearing that I'm missing a lot," Kokko says. The San Francisco-based company takes in information from thousands of licensed data sources, as well as public web sources like news reports, and automatically processes them to extract meaning on a sentence-by-sentence level.

How Many Arlington Asset Investment Corp (NYSE:AI)'s Analysts Are Bearish?