Azeem Azhar, CognitionX advisor and creator of Exponential View, commented on this fast-paced innovation in a recent Facebook post. He asked a few thought-provoking questions, one of which was “What breathtaking changes will occur as we apply prediction and optimisation to a wide range of problems?”
Check out his post and the interesting discussion in the comments. Tweet us your thoughts @Cognition_X or comment on the post if you’re friends with Azeem.
IBM and Salesforce on Monday afternoon revealed a sweeping global strategic partnership that aligns one iconic company’s multiyear turnaround effort with another’s staggering growth ambitions. According to the terms of the deal, IBM and Salesforce will integrate artificial intelligence platforms (Watson and Einstein, respectively) and some of their software and services (e.g. a Salesforce component to ingest The Weather Company’s meteorological data). IBM will also deploy Salesforce Service Cloud internally in a sign of goodwill.
Ginni Rometty, CEO of IBM, told Fortune that “this announcement is both strategic and significant. I do think it’s really going to take AI further into the enterprise. I think about 2017 as the year when we’re going to see AI hit the world at scale. It’s the beginning of an era that’s going to run a decade or decades in front of us.”
A new study from computer scientists has found that the online encyclopedia is a battleground where silent wars have raged for years.
Since Wikipedia launched in 2001, its millions of articles have been ranged over by software robots, or simply “bots”, that are built to mend errors, add links to other pages, and perform other basic housekeeping tasks.
In the early days, the bots were so rare they worked in isolation. But over time, the number deployed on the encyclopedia exploded with unexpected consequences. The more the bots came into contact with one another, the more they became locked in combat, undoing each other’s edits and changing the links they had added to other pages. Some conflicts only ended when one or other bot was taken out of action.
While some conflicts mirrored those found in society, such as the best names to use for contested territories, others were more intriguing. The researchers reveal that among the most contested articles were pages on former president of Pakistan Pervez Musharraf, the Arabic language, Niels Bohr and Arnold Schwarzenegger.
In a new study from the National Bureau of Economic Research, economists and computer scientists trained an algorithm to predict whether defendants were a flight risk from their rap sheet and court records using data from hundreds of thousands of cases in New York City. When tested on over a hundred thousand more cases that it hadn’t seen before, the algorithm proved better at predicting what defendants will do after release than judges.
Jon Kleinberg, a computer science professor at Cornell involved in the research, says one goal of the project was to show policymakers the potential benefits to society of using machine learning in the criminal justice system. “This shows how machine learning can help even in contexts where there’s considerable human expertise being brought to bear,” says Kleinberg, who worked on the project with researchers from Stanford, Harvard, and the University of Chicago.
Volkswagon has just presented Sedric, their first stab at self-driving cars. The car is fully electric, fully connected, and fully autonomous. Being fully autonomous, it does not even have a steering wheel, pedals, or a cockpit.
The company says that it is “the first automobile manufacturer to present an integrated mobility concept for mobility of the future in road traffic, including a Concept Car developed from scratch for autonomous driving. Sedric (self-driving car) provides a concrete insight into this innovative form of individual mobility that can be used by everyone, but which can nevertheless be geared to personal needs and aspirations – available at the touch of a button, easy, sustainable, convenient, and safe.”
Chinese Internet firm Baidu Inc. is rumored to plan to invest US$100 million in NextEV, a Chinese smart electric vehicle start-up backed by top global investors including Singapore’s Temasek Holdings, private equity giant TPG and Sequoia Capital.
Baidu is to participate in NextEV’s series C financing round, according to Chinese media reports citing insiders. Some existing investors are participating in the new funding round as well, said the reports. It’s unclear how much a stake will Baidu obtain in return.
It recently established a business unit to focus on self-driving technology and appointed its newly hired chief operation officer Lu Qi as the head of the unit, signalling the importance Baidu is placing on the sector.
Euler Hermes, the world’s leading trade credit insurer, today announced a pioneering partnership between its Digital Agency and Flowcast, a fintech company focused on revolutionizing trade and supply chain finance with AI. The announcement was made as the partners attended LendIt USA, a major lending and fintech conference in New York this week.
Flowcast will use its strength in analyzing transaction data to significantly evolve the concept by developing smart algorithms to create the foundation of an innovative underwriting solution within Single Invoice Cover. Benefits include improved working capital and financing along the supply chain.
Companies are scrambling to find enough programmers capable of coding for ML and deep learning. Are you ready? Here are five of Jane Luksich from Jaxenter‘s top picks for machine learning libraries for Java.
The number one pick on the list is Weka. Weka 3
is a fully Java-based workbench best used for machine learning algorithms. Weka is primarily used for data mining, data analysis, and predictive modelling. It’s completely free, portable, and easy to use with its graphical interface.
The other four libraries on the list are: Massive Online Analysis, Deeplearning4j, MALLET, and ELKI.
This work is on landmark localization using binarized approximations of Convolutional Neural Networks (CNNs). The authors’ goal is to design architectures that retain the groundbreaking performance of CNNs for landmark localization and at the same time are lightweight, compact and suitable for applications with limited computational resources.
To this end, they make the following contributions: (a) they are the first to study the effect of neural
network binarization on localization tasks, namely human pose estimation and face alignment. (b) Based on their analysis, they propose a novel hierarchical, parallel and multi-scale residual block architecture that yields large performance improvement over the standard bottleneck block when having the same number of parameters, thus bridging the gap between the original network and its binarized counterpart. (c) They present results for experiments on the most challenging datasets for human pose estimation and face alignment, reporting in many cases state-of-the-art performance. Code can be download from here.