AI & ML

It can take dozens of back-and-forth emails just to set up a quick meeting. Now, an artificial intelligence personal assistant can do it for you. Dennis Mortensen, CEO of x.ai, explains why you shouldn’t be afraid.

Goodbye to Personal Assistants

Not surprisingly, Joseph Sirosh, has big ambitions for his product portfolio at Microsoft which includes Azure ML, HDInsight and other tools. Chief among them is making it easy for mere mortals to consume these data services from the applications they're familiar with. Take Excel for example.

If a financial analyst can, with a few clicks, send data to a forecast service in the cloud, then get the numbers back, visualized on the same spreadsheet, that's a pretty powerful story, said Sirosh who is corporate VP of machine learning for Microsoft.

But as valuable as those applications and services are, more and more of the value to be derived from computation over time will be the data itself, not all those tech underpinnings.  "In the future a huge part of the value generated from computing will come from the data as opposed to storage and operating systems and basic infrastructure," he noted on this week's podcast. WHich is why one topic under discussion at next month's Structure Data show will be who owns all the data flowing betwixt and betweeen various systems, the internet of things etc.

When it comes to getting corporations running these new systems Microsoft may have an ace in the hole because so many of them already use key Microsoft tools -- Active Directory, SQL Server, Excel. That gives them a pretty good on-ramp to Microsoft Azure and its resident services. Sirosh makes a compelling case and we'll talk to him more on stage at Structure Data next month in New York City.

In the first half of the show, Derrick Harris and I talk about the Hadoop world has returned to its feisty and oh so interesting roots. When Pivotal announced its plan to offload support of Hadoop to Hortonworks and work with that company along with IBM, GE on  the Open Data Platform the response from Cloudera CEO Mike Olsen in a blog post with his take. 

Also on the docket, @WalmartLabs massive OpenStack production private cloud implementation.

Microsoft's machine learning guru on why data matters sooooo much

"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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The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation.
 
This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments and certain publicly traded cryptocurrencies/ digital assets for which the issuer has not provided permission for a16z to disclose publicly) is available at https://a16z.com/investments/.

Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information.

a16z Podcast: Making Sense of Big Data, Machine Learning, and Deep Learning

For 20 years James Barrat has created documentary films for National Geographic, the BBC, Discovery Channel, History Channel and public television. In 2000, during the course of his career as a film-maker, James interviewed Ray Kurzweil and Arthur C. Clarke. The latter interview not only transformed entirely Barrat’s views on artificial intelligence, but also made him write a book on the technological singularity called Our Final Invention: Artificial Intelligence and the End of the Human Era.

I read an advance copy of Our Final Invention and it is by far the most thoroughly researched and comprehensive anti-The Singularity is Near book that I have read so far. And so I couldn’t help it but invite James on Singularity 1 on 1 so that we can discuss the reasons for his abrupt change of mind and consequent fear or the singularity.

During our 70 minute conversation with Barrat we cover a variety of interesting topics such as: his work as a documentary film-maker who takes interesting and complicated subjects and makes them simple to understand; why writing was his first love and how he got interested in the technological singularity; how his initial optimism about AI turned into pessimism; the thesis of Our Final Invention; why he sees artificial intelligence more like ballistic missiles rather than video games; why true intelligence is inherently unpredictable “black box”; how we can study AI before we can actually create it; hard vs slow take-off scenarios; the positive bias in the singularity community; our current chances of survival and what we should do…

James Barrat on AI, the Singularity and Our Final Invention

Machine learning algorithms are a set of computerised techniques for recognising patterns in data and ‘learn’ to predict outcomes based on those patterns. These powerful techniques are transforming many aspects of our lives but the biggest changes are yet to come. Greater computing power, larger datasets and improvements to the algorithms are opening up many new opportunities. Personalised medicine, AI assistants and driverless cars are some of the most exciting developments which may soon be possible. But with the current hype it’s difficult to get to grips with what these systems can actually do and where their limitations lie. 

What can machine learning algorithms do and what can’t they do? What are the challenges and what lies ahead?

Our report explores in more detail what these algorithms are, what they are capable of doing and the key future challenges. Read the report: http://www.nesta.org.uk/machines-learn-wild
 
Photo credit: Robot Forest, Vincent Ferron, Flickr, CC by NC 2.0.

Machines That Learn in the Wild

In science-fiction, you’ll encounter a lot of computers that can feel... HAL 9000, Deckard, WALL-E. But what about computers that can really sense your emotions? MIT Professor Rosalind Picard takes a look at this artificial-feeling future.

For more innovative content, follow us:
twitter.com/IHubRadio
www.facebook.com/InnovationHubradio

Computers that Know You're Mad

O'Reilly's Ben Lorica chats with Alice Zheng about her background, techniques for evaluating machine learning models, how much math data scientists need to know, and the art of interacting with business users.

Bridging the Divide: Business Users and Machine Learning Experts

What is A.I. or artificial intelligence but the 'space of possible minds', argues Murray Shanahan, scientific advisor on the movie Ex Machina and Professor of Cognitive Robotics at Imperial College London.

In this special episode of the a16z Podcast brought to you on the ground from London, Shanahan -- along with journalist-turned-entrepreneur Azeem Azhar (who also curates The Exponential View newsletter on AI and more) and The Economist Deputy Editor Tom Standage (the author of several tech history books) -- we discuss the past, present, and future of A.I. ... as well as how it fits (or doesn't fit) with machine learning and deep learning.

But where are we now in the A.I. evolution? What players do we think will lead, if not win, the current race? And how should we think about issues such as ethics and automation of jobs without descending into obvious extremes? All this and more, including a surprise easter egg in Ex Machina shared by Shanahan, whose work influenced the movie.

a16z Podcast: Artificial Intelligence and the 'Space of Possible Minds'

Money Talk‬ : A discussion on “Second Phase of Gold Monetization Scheme”.
The participants are: Jayanta Roychoudhary, Business Editor, The Telegraph and Paranjoy Guha Thakurta, Senior Economic Journalist.

Money Talk‬ : A discussion on “Second Phase of Gold Monetization Scheme”.

This is my complete keynote at IVSZ - MTA in Budapest on Feb 10 2016 see https://youtu.be/jDviVFuJHgc for the original upload. My blog has the slide for downloading: http://bit.ly/1LPagkd

Please find the Event Video here: https://www.youtube.com/watch?v=veEkZZzGtSg&feature=em-subs_digest

The Digital Transformation of society, Futurist Keynote Speaker Gerd Leonhard at IVSZ Budapest

In this episode of the O’Reilly Data Show, I spoke with Mikio Braun, delivery lead and data scientist at Zalando. After spending previous years in academia, Braun recently made the decision to switch to industry. He shared some observations about building large-scale systems, particularly deploying data applications in production systems. Given his longstanding background as a machine learning researcher and practitioner, I wanted to get his take on topics like deep learning, hybrid systems, feature engineering, and AI applications.

Practical Machine Learning Techniques for Building Intelligent Applications

In this week's podcast QCon chair Wesley Reisz talks to Machine learning research scientist John Langford.  Topics include his Machine Learning system Vowpal Wabbit, designed to be very efficient and incorporating some of the latest algorithms in the space.  Vowpal Wabbit is used for news personalisation on MSN.  They also discuss how to get started in the field and it’s shift from academic research to industry use.

Why listen to this podcast:

 - Vowpal Wabbit is a ML system that attempts to incorporate some of the latest machine learning algorithms.
 - How to learn ML: take a class or two, get accustomed with learning theory and practice.
 - ML has moved from the research field into the industry, 4 out of 9 ICML tutorials coming from the industry.
 - It’s hard to predict when you have enough data.
 - AlphaGo is a milestone in artificial intelligence. It uses reinforcement learning, deep learning and existing moves played by Go masters.
 - Deep Learning is currently a disruptive technology in areas such a vision or speech recognition.
 - What’s trendy: Neural Networks, Reinforcement and Contextual Learning.
 - Machine Learning is being commoditized.
 
Notes and links can be found on http://bit.ly/2b4YNqQ

How to Approach Machine Learning

6m:12s To start learning Machine Learning, Langford recommends taking a class or two, mentioning the course by Andrew Ng and another course by Yaser S. Abu-Mostafa.
6m:50s It is recommended to get accustomed with learning theory to avoid some of the rookie's mistakes.

Quick scan our curated show notes on InfoQ. http://bit.ly/2atBFgk

You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. http://bit.ly/24x3IVq

InfoQ Podcast

Show Notes

John Langford on Vowpal Wabbit, Used by MSN, and Machine Learning in Industry

In this week's podcast InfoQ’s editor-in-chief Charles Humble talks to Data Scientist Cathy O’Neil. O'Neil is the author of the blog mathbabe.org. She was the former Director of the Lede Program in Data Practices at Columbia University Graduate School of Journalism, Tow Center and was employed as Data Science Consultant at Johnson Research Labs. O'Neil earned a mathematics Ph.D. from Harvard University. Topics discussed include her book “Weapons of Math Destruction,” predictive policing models, the teacher value added model, approaches to auditing algorithms and whether government regulation of the field is needed.

Why listen to this podcast:

 - There is a class of pernicious big data algorithms that are increasingly controlling society but are not open to scrutiny.
 - Flawed data can result in an algorithm that is, for instance, racist and sexist.  For example, the data used in predictive policing models is racist. But people tend to be overly trusting of algorithms because they are mathematical.
 - Data scientists have to make ethical decisions even if they don’t acknowledge it. Often problems stem from an abdication of responsibility.
 - Auditing for algorithms is still a very young field with ongoing academic research exploring approaches.
 - Government regulation of the industry may well be required.

Notes and links can be found on http://bit.ly/2eYVb9q

Weapons of math destruction

0m:43s - The central thesis of the book is that whilst not all algorithms are bad, there is a class of pernicious big data algorithms that are increasingly controlling society.
1m:32s - The classes of algorithm that O'Neil is concerned about - the weapons of math destruction - have three characteristics: they are widespread and impact on important decisions like whether someone can go to college or get a job, they are somehow secret so that the people who are being targeted don’t know they are being scored or don’t understand how their score is computed; and the third characteristic is they are destructive - they ruin lives.
2m:51s - These characteristics undermine the original intention of the algorithm, which is often trying to solve big society problems with the help of data.

More on this:

Quick scan our curated show notes on InfoQ. http://bit.ly/2eYVb9q

You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. bit.ly/24x3IVq

Cathy O'Neil on Pernicious Machine Learning Algorithms and How to Audit Them

In this session of the Super Data Science Podcast, I chat with machine learning expert Hadelin de Ponteves about his work at Google, Canal+ and what career implications may come from the rapid growth of the Data Science field.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/2

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 002 : Machine Learning, Recommender Systems and The Future of Data with Hadelin de Ponteves

In this session of the Super Data Science Podcast, I chat with Neuroscience PhD Turned Retail Data Analyst Muhsin Karim. You will know what is neuroscience,  learn more about machine learning and discover some surprising implications from Moore’s Law.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/9

SDS 009 : Neuroscience, Machine Learning and Moore’s Law with Muhsin Karim, Phd

In this episode of the SuperDataScience Podcast, I chat with Chief Data Scientist Ben Taylor. You will learn about the role of AI in recruitment, know about a crash course in deep learning and discuss on where machines can outperform humans. 

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/29

SDS 029: Dive Into Deep Learning and Find Out Where Machines Can Outperform Humans With Ben Taylor

At the episode 167. we interviewed Bradford Cross (Google, Flightcaster, Prismatic) who leads machine learning and big data venture capital fund http://www.ceai.io. We have been discussing various topics - Scala vs. Clojure, Prismatic acquistion by LinkedIn, machine learning at scale etc. One of the key topics was about a culture in startups distributed between Sillicon Valley and Central Europe. An excellent talk worth to listen.

CZPodcast

CZ Podcast 167 - Machine learning startups

A lot of machine learning startups initially feel a bit of “impostor syndrome” around competing with big companies, because (the argument goes), those companies have all the data; surely we can’t beat that! Yet there are many ways startups can, and do, successfully compete with big companies. You can actually achieve great results in a lot of areas even with a relatively small data set, argue the guests on this podcast, if you build the right product on top of it.

So how do you go about building the right product (beyond machine-learning algorithms in academic papers)? It’s about the whole system, the user experience, transparency, domain expertise, choosing the right tools. But what do you build, what do you buy, and do you bother to customize? Jensen Harris, CTO and co-founder of Textio, and AJ Shankar, CEO and co-founder of Everlaw, share their lessons learned here in this episode of the a16z Podcast — including what they wish they’d known early on.

Because, observes moderator (and a16z board partner) Steven Sinofsky, “To achieve product market fit, there’s a whole bunch of stuff beyond a giant corpus of data, and the latest deep learning algorithm.” Machine learning is an ingredient, part of a modern software-as-a-service company; going beyond the hype, it’s really about figuring out the problem you’re trying to solve… and then figuring out where machine learning fits in (as opposed to the other way around). Customers are paying you to help solve a problem for them, after all.

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The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation.
 
This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments and certain publicly traded cryptocurrencies/ digital assets for which the issuer has not provided permission for a16z to disclose publicly) is available at https://a16z.com/investments/.

Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information.

a16z Podcast: The Product Edge in Machine Learning Startups

In this episode of the Data Show, I spoke with Aurélien Géron, a serial entrepreneur, data scientist, and author of a popular, new book entitled "Hands-on Machine Learning with Scikit-Learn and TensorFlow." Géron’s book is aimed at software engineers who want to learn machine learning and start deploying machine learning models in real-world products.

Becoming a Machine Learning Engineer

In this episode of the SuperDataScience Podcast, I chat with Head of Applied Machine Learning Ryan Compton. You will learn about how a Neural Network Filters NSFW images differently from humans, you will talk about the different applications of Neural Network Image Filters, discuss about why artificial intelligence is used in the world and how it is helping organizatons deliver better services; and you will also learn about the 2 Growth Areas in AI - the automation and pushing boundaries.

If you enjoyed this episode, check out show notes, resources, and more at http://www.superdatascience.com/57

AI & ML

Derick D'souza