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  • There has been a 14X increase in the number of active AI startups since 2000. Crunchbase, VentureSource, and Sand Hill Econometrics were also used for completing this analysis with AI startups in Crunchbase cross-referenced to venture-backed companies in the VentureSource database. Any venture-backed companies from the Crunchbase list that were identified in the VentureSource database were included.

  • The share of jobs requiring AI skills has grown 4.5X since 2013., The growth of the share of US jobs requiring AI skills on the Indeed.com platform was calculated by first identifying AI-related jobs using titles and keywords in descriptions. Job growth is a calculated as a multiple of the share of jobs on the Indeed platform that required AI skills in the U.S. starting in January 2013. The study also calculated the growth of the share of jobs requiring AI skills on the Indeed.com platform, by country. Despite the rapid growth of the Canada and UK. AI job markets, Indeed.com reports they are respectively still 5% and 27% of the absolute size of the US AI job market.

  • Machine Learning, Deep Learning and Natural Language Processing (NLP) are the three most in-demand skills on Monster.com. Just two years ago NLP had been predicted to be the most in-demand skill for application developers creating new AI apps. In addition to skills creating AI apps, machine learning techniques, Python, Java, C++, experience with open source development environments, Spark, MATLAB, and Hadoop are the most in-demand skills. Based on an analysis of Monster.com entries as of today, the median salary is $127,000 in the U.S. for Data Scientists, Senior Data Scientists, Artificial Intelligence Consultants and Machine Learning Managers.

  • Error rates for image labeling have fallen from 28.5% to below 2.5% since 2010. AI’s inflection point for Object Detection task of the Large Scale Visual Recognition Challenge (LSVRC) Competition occurred in 2014. On this specific test, AI is now more accurate than human These findings are from the competition data from the leaderboards for each LSVRC competition hosted on the ImageNet website.

  • Global revenues from AI for enterprise applications is projected to grow from $1.62B in 2018 to $31.2B in 2025 attaining a 52.59% CAGR in the forecast period. Image recognition and tagging, patient data processing, localization and mapping, predictive maintenance, use of algorithms and machine learning to predict and thwart security threats, intelligent recruitment, and HR systems are a few of the many enterprise application use cases predicted to fuel the projected rapid growth of AI in the enterprise. Source: Statista.

  • 84% of enterprises believe investing in AI will lead to greater competitive advantages. 75% believe that AI will open up new businesses while also providing competitors new ways to gain access to their markets. 63% believe the pressure to reduce costs will require the use of AI. Source: Statista.

  • 87% of current AI adopters said they were using or considering using AI for sales forecasting and for improving e-mail marketing. 61% of all respondents said that they currently used or were planning to use AI for sales forecasting. The following graphic compares adoption rates of current AI adopters versus all respondents. Source: Statista.

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Cloud-based, AI-powered location technology is creating the highly accurate and always up-to-date maps that can revolutionize everything from autonomous cars to connected cities. To learn more about the application of data-enriched mapping to industries from retail to automotive, manufacturing, transportation and city planning, don’t miss this VB Live event!

Register here for free.

Location is at the heart of everything: it’s the nexus between a device or an individual and the environment they interact with, and it can become the foundation of a smarter society. Location data is powered by cloud capabilities: global maps, traffic information and hundreds of millions of connected devices brought together to create the most up-to-date maps and power the “The Location of Things.”

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A random-code technique has been used at Arecibo to obtain delay-Doppler radar images of the full disk of Mercury. Anomalously bright features were found at the north and south poles. The north polar feature is oblong (4° by 8°) and offset from the pole. The smaller south polar feature is mostly confined to the floor of the crater Chao Meng-Fu. The polar locations and radar properties of these features indicate that they may be produced by volume scattering in ice. The images also reveal a variety of more subdued reflectivity features ranging in size from hundreds to thousands of kilometers; some of these appear to have an impact origin.

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A trio of satellites studying our planet’s magnetic field have shown details of the steady swell of a magnetic field produced by the ocean’s tides.

Four years of data collected by the European Space Agency’s (ESA) Swarm mission have contributed to the mapping of this ‘other’ magnetic field, one that could help us build better models around global warming.

Physicist Nils Olsen from the Technical University of Denmark presented the surprising results at this year’s European Geosciences Union meeting in Vienna, explaining how his team of researchers managed to detail such a faint signature.

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One of the #brain’s mysteries is how exactly it reorganizes new #information as you learn new tasks. The standard to date was to test how neurons learned new behavior one #neuron at a time. Carnegie Mellon University and the University of Pittsburgh decided to try a different approach. They looked at the population of neurons to see how they worked together while #learning a new behavior. Studying the intracortical population activity in the primary motor cortex of rhesus macaques during short-term learning in a brain–computer interface (BCI) task, they were able to study the reorganization of population during learning. Their new research indicates that when the brain learns a new activity that it is less flexible than previously thought. The researchers were able to draw strong hypothesis about neural reorganization during learning by using BCI. Through the use of BCI the mapping between #neural activity and learning is completely known.

“In this experimental paradigm, we’re able to track all of the neurons that can lead to behavioral improvements and look at how they all change simultaneously,” says Steve Chase, an associate professor of biomedical engineering at Carnegie Mellon and the Center for the Neural Basis of #Cognition. “When we do that, what we see is a really constrained set of changes that happen, and it leads to this suboptimal improvement of performance. And so, that implies that there are limits that constrain how flexible your brain is, at least on these short time scales.”

It is often challenging to learn new tasks quickly that require a high level of proficiency. Neural plasticity is even more constrained than previously thought as results of this research indicate.

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Google Cloud and AGI (a.k.a. Analytical Graphics Inc.) have gotten on board with the B612 Asteroid Institute to develop a cloud-based platform for keeping track of asteroid discoveries.

The two companies have become technology partners for the Asteroid Decision Analysis and Mapping project, or ADAM, which aims to provide the software infrastructure for analyzing the trajectories of near-Earth objects, identifying potential threats, and sizing up the scenarios for taking action if necessary.

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A startup working to better understand the relationship our gut has with our brain has raised another $66 million.

New York-based Kallyope raised its series B round from new investors Two Sigma Ventures and Euclidean Capital. They were joined by Polaris Partners, Illumina Ventures, Lux Capital and others that had invested in Kallyope’s $44 million series A round in 2015.

Kallyope is trying to figure out how exactly the brain interacts with the gut by mapping it out. By collecting sequencing information about cells in the gut, for example, Kallyope can better figure out how they’re connected to neurons in the brain in a series of circuits. Understanding that relationship could lead to pills that could interact with the gut’s signals and in turn pass that message along to the brain.

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The Netflix series takes place hundreds of years in the future, but references versions of technology that have been in development for years, like brain mapping, human and AI neural links, and mind uploading to computers. Millions of dollars has been bumped into technological ideas that promise, one day, our brains will be turned digital. That said, there are those who believe the human mind is too complex, and our consciousness too nuanced, to be recreated in a digital product. And none of that even goes into what would happen if someone’s digitized mind was placed into real human flesh.

Will we ever be able to upload our minds into other bodies? Furthermore, should we? And honestly, if we ever achieved such a feat, could we even call ourselves human anymore? On this week’s Giz Asks, we reached out to experts in neuroscience, philosophy and futurism.

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for the 2017 tallinn architecture biennale, noumena has presented its installation based on the future of robots and its adaptability with the environment. deep learning has paved the way for machines to expand beyond narrow capabilities to soon achieving human-level performance on intellectual tasks. however, as artificial intelligence — A.I. — establishes its place within humans, society will need to develop a framework for both to thrive. a new form of artificial life will emerge, finding space at the peripheries of humanity in order to not compete for human-dominated resources. A.I. will attempt to improve its operating surroundings to not just survive but be self-sustaining, forming the basis of a civilization constrained at the intersection of nature and technology.


image © tõnu tunnel.

barcelonian based practice noumena has developed a framework to build this narrative based on the cross disciplinary intersection of computational design, mechanical and electronic design, rapid prototyping interaction and mapping. nowadays, computing tools as well as rapid prototyping machines allow to have a quick practical feedback on design solutions and to iterate experimenting different possibility at the same time giving the chance to choose and custom a functional part.

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