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Category: information science – Page 271

  • 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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Nature article presents an AI developed by Google’s Medical Brain team which outperforms hospitals’ own warning system in predicting the death risk among hospit…al patients.

Google’s Medical Brain team is now training its AI to predict the death risk among hospital patients — and its early results show it has slightly higher accuracy than a hospital’s own warning system.

Bloomberg describes the healthcare potential of the Medical Brain’s findings, including its ability to use previously unusable information in order to reach its predictions. The AI, once fed this data, made predictions about the likelihood of death, discharge, and readmission.

In a paper published in Nature in May, from Google’s team, it says of its predictive algorithm:

Continue reading “Google says its AI is better at predicting death than hospitals” | >

It’s not unrealistic to think that 80% of what doctors do will be replaced by algorithms and artificial intelligence. The idea, evangelized by venture capitalist Vinod Khosla two years ago, is that machines can more accurately diagnosis us — and that will reduce deadly medical errors and free doctors up to do other things.

The bottom line: We’re getting closer to this reality. Algorithms, for example, can already diagnose diseases from imaging scans better than human radiologists. Computers possibly could take over the entire radiology specialty.

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Robots aren’t playing professional soccer just yet, but they can certainly help predict it! With the FIFA World Cup kicking off, San Francisco-based tech firm Unanimous A.I. has used its considerable artificial intelligence expertise to predict the outcome of the 32-team men’s soccer tournament. Given that the startup has previously predicted the Super Bowl results successfully right down to the exact final score, we totally think this is worth taking seriously.

“These predictions were generated using swarm A.I. technology,” Louis Rosenberg, founder and CEO of Unanimous A.I., told Digital Trends. “This means it uses a unique combination of human insights and artificial intelligence algorithms, resulting in a system that is smarter than the humans or the machines could be on their own. It works by connecting a group of people over the internet using A.I. algorithms, enabling them to think together as a system, and converge upon predictions that are the optimized combination of their individual knowledge, wisdom, instincts, and intuitions.”

The technology is modeled on the remarkable abilities of swarms in nature, such as swarms of bees, schools of fish, or flocks of birds. These natural swarms combine the insights of large groups in optimized ways. Unanimous’ swarms utilize this same principle to answer complex questions — such as giving precise probability-based outcomes on each game in the World Cup.

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Recommended Books ➤

📖 Life 3.0 — http://azon.ly/ij9u
📖 The Master Algorithm — http://azon.ly/excm
📖 Superintelligence — http://azon.ly/v8uf

This video is the ninth in a multi-part series discussing computing and the second discussing non-classical computing. In this video, we’ll be discussing what quantum computing is, how it works and the impact it will have on the field of computing.

[0:28–6:14] Starting off we’ll discuss, what quantum computing is, more specifically — the basics of quantum mechanics and how quantum algorithms will run on quantum computers.

Continue reading “What Is Quantum Computing (Future of AI Computing)” | >

Autonomous deliveries and drones

UPS execs insist that the UPS driver is a core element to its success and the face of the company, but they have tested the use of drone deliveries for some applications including dropping essential supplies in Rwanda and demonstrating how medicine could be delivered to islands. In rural areas, where drones have open air to execute deliveries and the distance between stops makes it challenging for the drivers to be efficient, drones launched from the roofs of UPS trucks offer a solid solution to cut costs and improve service. Drones could also be deployed in UPS sorting facilities and warehouses to get items on high shelves or in remote areas.

The technology used by UPS generates a cache of data that opens up even more opportunities to become more efficient, improve the customer experience, innovate delivery solutions, and more. From optimizing the UPS network to driving operational improvements, big data and artificial intelligence are at the core of UPS’s business performance.

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Recommended Books ➤

📖 Life 3.0 — https://amzn.to/2KZdRU0
📖 The Master Algorithm — https://amzn.to/2jV1egi
📖 Superintelligence — https://amzn.to/2rCXzqQ

This video is the eleventh in a multi-part series discussing computing. In this video, we’ll be discussing what cognitive computing is and the impact it will have on the field of computing.

[0:28–5:09] Starting off we’ll discuss, what cognitive computing is, more specifically – the difference between current computing Von Neuman architecture and more biologically representative neuromorphic architecture and how these two paired together will yield massive performance and efficiency gains!

Continue reading “What Is Cognitive Computing (How AI Will Think)” | >

The point of the experiment was to show how easy it is to bias any artificial intelligence if you train it on biased data. The team wisely didn’t speculate about whether exposure to graphic content changes the way a human thinks. They’ve done other experiments in the same vein, too, using AI to write horror stories, create terrifying images, judge moral decisions, and even induce empathy. This kind of research is important. We should be asking the same questions of artificial intelligence as we do of any other technology because it is far too easy for unintended consequences to hurt the people the system wasn’t designed to see. Naturally, this is the basis of sci-fi: imagining possible futures and showing what could lead us there. Issac Asimov gave wrote the “Three Laws of Robotics” because he wanted to imagine what might happen if they were contravened.

Even though artificial intelligence isn’t a new field, we’re a long, long way from producing something that, as Gideon Lewis-Kraus wrote in The New York Times Magazine, can “demonstrate a facility with the implicit, the interpretive.” But it still hasn’t undergone the kind of reckoning that causes a discipline to grow up. Physics, you recall, gave us the atom bomb, and every person who becomes a physicist knows they might be called on to help create something that could fundamentally alter the world. Computer scientists are beginning to realize this, too. At Google this year, 5,000 employees protested and a host of employees resigned from the company because of its involvement with Project Maven, a Pentagon initiative that uses machine learning to improve the accuracy of drone strikes.

Norman is just a thought experiment, but the questions it raises about machine learning algorithms making judgments and decisions based on biased data are urgent and necessary. Those systems, for example, are already used in credit underwriting, deciding whether or not loans are worth guaranteeing. What if an algorithm decides you shouldn’t buy a house or a car? To whom do you appeal? What if you’re not white and a piece of software predicts you’ll commit a crime because of that? There are many, many open questions. Norman’s role is to help us figure out their answers.

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Recently we saw a new “Master algorithm” that could be used to create the first generation of super intelligent machines, and now a team of researchers from Maryland, USA, announced this week that they’ve invented a general Artificial Intelligence (AI) way for machines to identify and process 3D images that doesn’t require humans to go through the tedium of inputting specific information that accounts for each and every instance, scenario, difference, change and category that could crop up, and they claim it’s a world first, even though it follows on from a not too dissimilar breakthrough from Google DeepMind whose own platform, Alpha Zero, recently taught itself a mix of board games including chess to a grand master level, in just four hours.

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