Feng Zhang, a pioneer of the revolutionary CRISPR gene-editing technology, TAL effector proteins, and optogenics, is the recipient of the 2017 $500,000 Lemelson-MIT Prize, the largest cash prize for invention in the United States. Prior to harnessing CRISPR-Cas9, Zhang engineered microbial TAL effectors (TALEs) for use in mammalian cells, working with colleagues at Harvard University, authoring multiple publications on the subject and becoming a co-inventor on several patents on TALE-based technologies. Zhang was also a key member of the team at Stanford University that harnessed microbial opsins for developing optogenetics, which uses light signals and light-sensitive proteins to monitor and control activity in brain cells. Zhang's numerous scientific discoveries and inventions, as well as his commitment to mentorship and collaboration, earned him the Lemelson-MIT Prize, which honors outstanding mid-career inventors who improve the world through technological invention and demonstrate a commitment to mentorship in science, technology, engineering and mathematics (STEM).
IBM created its first artificially intelligent computer IBM Watson. Now, there are several devices and applications available in the market which can record stress level and predict probable health issues. Creating a network of connected devices will make human lives faster and more convenient. It aims to make robots better surgical assistant using machine learning and advanced image processing.
This makes xgboost at least 10 times faster than existing gradient boosting implementations. A simple method to convert categorical variable into numeric vector is One Hot Encoding. Compared to other machine learning techniques, I find implementation of xgboost really simple. In this post, I discussed various aspects of using xgboost algorithm in R. Most importantly, you must convert your data type to numeric, otherwise this algorithm won't work.
A few months ago, my company, CrowdFlower, ran a machine learning competition on Kaggle. Until we're replaced by robots, this is going to be the machine learning challenge of the next decade. But that's still on the order of 30,000 miles between potential crashes, while human drivers go on the order of 1 million miles between potential crashes and 100 million miles between fatal crashes. Companies no longer need a Google-size R&D budget to make machine learning applicable to their business.
Right after the start of the Kaggle competition participants started sharing interesting findings about the data set. It is very important to know in advance in case the duplicates' distribution is different in test and training data sets since the quality metric used in this solution is very sensitive to those distribution changes. Let's imagine the data set contains only seven records: Now we can calculate the number of "common neighbours" for every question pair from the data set. Modern deep learning models are represented by deep neural networks that get raw data as an input (questions' texts) and produce the necessary features themselves.
Bring your laptop, learn how to use OpenML in tutorials, and create something great that pushes the scientific community (and yourself) forward. Anything goes, from a cool extension of OpenML itself to solving a data-driven problem in your scientific domain. We also offer tutorials and inspirational invited talks.
Jason Lui 7,681 views IBM Watson presents Soul Machines, LENDIT Conference 2017 (Professional Camera) - Duration: 5:12. IBM Watson Internet of Things 1,187 views The future of AI is now - Damion Heredia (IBM Watson and Cloud Platform) - Duration: 4:36. IBM Watson Internet of Things 16,829 views IBM Watson IoT Platform and Blockchain: a trade finance example - Duration: 5:51. IBM Watson presents Soul Machines, LENDIT Conference 2017 (Professional Camera) - Duration: 5:12.
The St Andrew's team found that when the chess board reached 1000 squares by 1000, computer programmes could no longer cope with the vast number of options. This means that they sank into a potentially eternal struggle akin to the fictional super computer Deep Thought in Douglas Adams' Hitchhiker's Guide to the Galaxy. The St Andrew's team found that when the chess board reached 1000 squares by 1000, computer programmes could no longer cope with the vast number of options. This means that they sank into a potentially eternal struggle akin to the fictional'super computer' Deep Thought in Douglas Adams' Hitchhiker's Guide to the Galaxy.
Rosa recently took steps to scale up the research on general AI by founding the AI Roadmap Institute and launching the General AI Challenge. In some rounds, participants will be tasked with designing algorithms and programming AI agents. The Challenge kicked off on 15 February with a six-month "warm-up" round dedicated to building gradually learning AI agents. The tasks were specifically designed to test gradual learning potential, so they can serve as guidance for the developers.
Last week, an artificial intelligence bot created by the Elon Musk-backed start-up OpenAI defeated some of the world's most talented players of Dota 2, a fast-paced, highly complex, multiplayer online video game that draws fierce competition from all over the globe. Danylo "Dendi" Ishutin, one of the game's top players, was defeated twice by his AI competition, which felt "a little like human, but a little like something else," he said, according to the Verge. Tesla chief executive Elon Musk hailed the bot's achievement in historic fashion on Twitter before going on to once again express his concerns about artificial intelligence, which he said poses "vastly more risk than North Korea." Vastly more complex than traditional board games like chess & Go.