Machine intelligence is here, and we’re already using it to make subjective decisions. But the complex way AI grows and improves makes it hard to understand and even harder to control. In this cautionary talk, techno-sociologist Zeynep Tufekci explains how intelligent machines can fail in ways that don’t fit human error patterns — and in ways we won’t expect or be prepared for. "We cannot outsource our responsibilities to machines," she says. "We must hold on ever tighter to human values and human ethics."
"The actual path of a raindrop as it goes down the valley is unpredictable, but the general direction is inevitable," says digital visionary Kevin Kelly — and technology is much the same, driven by patterns that are surprising but inevitable. Over the next 20 years, he says, our penchant for making things smarter and smarter will have a profound impact on nearly everything we do. Kelly explores three trends in AI we need to understand in order to embrace it and steer its development. "The most popular AI product 20 years from now that everyone uses has not been invented yet," Kelly says. "That means that you’re not late."
a16z Podcast: Making Sense of Big Data, Machine Learning, and Deep Learning by a16z | Free Listening on SoundCloud
a16z Podcast: Making Sense of Big Data, Machine Learning, and Deep Learning
published on 2015/05/01 21:50:55 +0000
"Machine learning is to big data as human learning is to life experience," says Christopher Nguyen, the co-founder and CEO of big data intelligence company Adatao. Sure, but then, what IS big data? (especially as it’s become a buzzword that captures so many things)…
On this episode of the a16z Podcast, Nguyen puts on his former computer science professor hat to describe ‘big data’ in relation to ‘machine learning’ — as well as what comes next with ‘deep learning’. Finally, the former Google exec shares how Hadoop and Spark evolved from the efforts of companies dealing with massive amounts of real-time information; what we need to make machine learning a property of every application (why would we even want to?); and how we can make all this intelligence accessible to everyone.
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Why should you listen to this machine learning podcast?
Machine learning is one of the fastest-growing arms of the domain of artificial intelligence. It has far-reaching consequences and in the next couple of years, we will be seeing every industry deploying the principles of artificial intelligence, machine learning and deep learning technologies at scale.
Who should listen to this what is machine learning audio?
This machine learning audio is for everybody right from professionals in analytics, data science domains, eCommerce, or in search engine domains. If you are a Software professional looking for a career switch and fresh graduates then also you can listen to this audio and also take up an online machine learning course by visiting https://intellipaat.com/machine-learning-certification-training-course/
Why machine learning is important?
Machine learning might just be one of the most important fields of science that we are just moving towards. It differs from other science in the sense that this is one of the one domains where the input and output are not directly correlated and neither do we provide the input for every task that the machine will perform. It is more about mimicking how humans think and solving real-world problems like humans without actually the intervention of humans. It focuses on developing computer programs that can be taught to grown and change when exposed to data.
Free Resources on Machine Learning:
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Data science is a broad topic with numerous subfields such as data engineering and machine learning.
Yad Faeq returns to the podcast to discuss data science at a high level, and rescue Software Engineering Daily from the threat of the hype vortex.
Yad is a software engineer, currently working on machine learning applications. Yad first appeared on Software Engineering Daily on Episode 0, the inaugural show.
What algorithms does a data scientist need to know?
How would you architect a machine learning system for weather analytics?
What is deep learning?
How has Kaggle changed data science?
As machine learning comes further into our lives, does our world become more predetermined?
Yad Faeq Homepage
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There is a need for more data scientists to make sense of the vast amounts of data we produce and store.
Dataquest is an in-browser platform for learning data science that is tackling this problem.
Vik Paruchuri is the founder of Dataquest. He was previously a machine learning engineer at EdX and before that a U.S. diplomat.
What is data science?
How does data science compare to software engineering?
How does someone new to data science go about starting off at Kaggle?
In machine learning, there is unsupervised learning and supervised learning. Could you contrast these two?
What are the biggest world problems that will be solved with data science?
How to actually learn data science
Dr. Robert Elliott Smith works part time as a Senior Research Fellow of Computer Science at University College London, and as Chief Technology Office for BOXARR. He is a founding member of The UCL Centre for The Study of Decision-Making Uncertainty and also interested in complex-systems-based artificial intelligence.
In this talk, he discusses his new book "Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All", touching on his personal story and the history of machine learning methods. Having worked in artificial intelligence for over 30 years, he asks: what if the questionable morals of machines are not just the result of programmer or data prejudice, but there’s something more fundamental and explicitly mechanical at play, something inherent in the technology itself?
Find a copy of "Rage Inside the Machine" here: https://goo.gle/2JWaJsp
A.I., artificial intelligence, has had a big run in Hollywood. The computer Hal in Kubrick’s “2001” was fiendishly smart. And plenty of robots and server farms beyond HAL. Real life A.I. has had a tougher launch over the decades. But slowly, gradually, it has certainly crept into our lives.
Think of all the “smart” stuff around you. Now an explosion in Big Data is driving new advances in “deep learning” by computers. And there’s a new wave of excitement.
Guests: Yann LeCun, professor of Computer Science, Neural Science, and Electrical and Computer Engineering at New York University.
Peter Norvig, director of research at Google Inc.
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“Changing anything changes everything.”
Technical debt, referring to the compounding cost of changes to software architecture, can be especially challenging in machine learning systems.
D. Sculley is a software engineer at Google, focusing on machine learning, data mining, and information retrieval. He recently co-authored the paper Machine Learning: The High Interest Credit Card of Technical Debt.
How do you define technical debt?
Why does technical debt tend to compound like financial debt?
Is machine learning the marriage of hard-coded software logic and constantly changing external data?
What types of anti-patterns should be avoided by machine learning engineers?
What is a decision threshold in a machine learning system?
What advice would you give to organizations that are building their prototypes and product systems in different languages?
Adapter pattern and glue code
D’s research page
Are we nearing the singularity, the point where philosophers say the computer programs we create will be smarter than us?
Artificial intelligence is all around us. In phones, in cars, in our homes. Voice recognition systems, predicative algorithms, GPS. Sometimes they may not work very well, but they are improving all the time, you might even say they are learning.
Come on an entertaining journey through the ethics of artificial intelligence or AI, the science behind intelligent computer programs and robotics. Some software engineers think about the philosophy of the artificial intelligence they are creating, others really don’t care.