- The Search for a New Test of Artificial IntelligenceResearchers need new ways to distinguish artificial intelligence from the natural kind In 1950 Alan Turing devised a thought experiment that has since been revered as the ultimate test of machine intelligence.
- He called it the “imitation game,” but most people know it as the Turing test.
- Anticipating what we now call chat bots—computer programs that masquerade as humans—Turing envisioned a contest in which a machine tries to trick an interrogator into believing it is human, answering questions about poetry and deliberately making mistakes about arithmetic.
- Today, in the eyes of the general public, the Turing test is often seen as a kind of a Rubicon, a measure of whether machines have truly arrived.
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Researchers need new ways to distinguish artificial intelligence from the natural kind
Continue reading “The Search for a New Test of Artificial Intelligence”
- While there will be the usual unveiling of select smartphones and tablets, MWC has dramatically increased the presence of drones and autonomous vehicles as it seeks to expand the concept of what we mean by the word “mobile.”
- In addition, the conference has added multiple sessions on artificial intelligence, as it relates to vehicles and drones, as well as other mobile uses.
- This year, China’s DJI, the world’s largest drone company, is organizing its first major MWC press conference, tucked between Huawei and Samsung.
- “The advanced wireless capabilities of 5G … will usher in new use cases necessary to fulfill our vision for increasingly connected and autonomous vehicles,” said Kim Jin-yong, executive vice president of VC Smart Business Unit at LG Electronics, in a statement.
- In many cases, drones and autonomous vehicles share a common link in terms of their use of artificial intelligence.
If you’re looking for the future of mobile, it may be time to stop staring at the palm of your hand and look instead toward the open roads and sky.
Continue reading “Autonomous vehicles, drones, and AI will dominate Mobile World Congress 2017”
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- The argument against artificial intelligence (AI) is driven by fear.
- The most realistic fear today is that AI will take people’s jobs.
- Undoubtedly technology is taking people’s jobs in droves.
- Google is a great example of machine learning that many people use every day and it truly does make life easier.
- While the fear of job loss is understandable, there is another point to make: because of artificial intelligence many people are currently doing jobs that weren’t available even just a few years back.
According to Stephen Hawkings we do have reason to beware of the consequences of artificial intelligence (AI) including the possibility of the end of the human race.
Continue reading “Why the Benefits of Artificial Intelligence Outweigh the Risks”
- “Cognitive and cloud are not separate phenomena.
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Tap into the most advanced cloud technology in the market today at IBM InterConnect 2017, March 19–23 in Las Vegas.
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- From First Principles With Pure Python and
Use them on Real-World Datasets
You must understand algorithms to get good at machine learning.
- In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math and learn exactly how machine learning algorithms work.
- I’ve written books on algorithms, won and ranked in the top 10% in machine learning competitions, consulted for startups and spent a long time working on systems for forecasting tropical cyclones.
- (yes I have written tons of code that runs operationally)
I get a lot of satisfaction helping developers get started and get really good at machine learning.
- I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.
Discover How to Code Machine Algorithms
From First Principles With Pure Python and
Use them on Real-World Datasets
You must understand algorithms t…
Continue reading “Book: Machine Learning Algorithms From Scratch”
- Since deep learning loves math, we’re going to represent each word as a d-dimensional vector.
- Extracting the rows from this matrix can give us a simple initialization of our word vectors.
- The above cost function is basically saying that we’re going to add the log probabilities of ‘I’ and ‘love’ as well as ‘NLP’ and ‘love’ (where ‘love’ is the center word in both cases).
- One Sentence Summary: Word2Vec seeks to find vector representations of different words by maximizing the log probability of context words given a center word and modifying the vectors through SGD.
- Bonus: Another cool word vector initialization method: GloVe (Combines the ideas of coocurence matrices with Word2Vec)
This edition of Deep Learning Research Review explains recent research papers in Natural Language Processing (NLP). If you don’t have the time to read the top papers yourself, or need an overview of NLP with Deep Learning, this post is for you.
Continue reading “Deep Learning Research Review: Natural Language Processing”