Calling All Machine Learning Researchers


(Image: NicoElNino/iStockphoto)

(Image: NicoElNino/iStockphoto)

Analytics, machine learning, and artificial intelligence may not be new, but AI and machine learning are experiencing a rapid uptake among organizations. The interest by businesses and other enterprises has led one organization to embark on a bit of a reality check and an effort to document the state of AI and machine learning and projects so far.

"Many people are starting to ask what a world with intelligent computers will look like," Peter Eckersley and Gennie Gebhart of the Electronic Frontier Foundation write in a blog post. "But what is the ratio of hype to real progress? What kind of problems have been well solved by current machine learning techniques which ones are close to being solved, and which ones remain exceptionally hard?"

[Learn more about AI and Machine Learning. The most recent edition of A2 Academy available now on-demand.]

The EFF, an international non-profit digital rights advocacy and legal defense organization, is launching a new program called the EFF AI Progress Measurement experiment. This pilot project collects problems and metrics/datasets from the AI research literature and tracks progress on them, according to the EFF.

"At EFF, we're ultimately most interested in how this data can influence our understanding of the likely implications of AI," the EFF writes on the project page. "To begin with, we are focused on gathering it."

The initial project is here, and it documents many of the AI and machine learning projects to date.

The EFF is looking for help and contributors to this project. The organization is calling on machine learning researchers to provide feedback and contribute to the effort. So far EFF has drawn data from sources including blog posts, web sites, and review articles, and collected the information on Github.

Now EFF is asking machine learning researchers to weigh in on the following questions: Is this information useful to the machine learning community? And, what important problems, datasets, and results are missing from what's there so far?

Let us know in the comments what you think of the state of machine learning to date, and of EFF's effort to document it.

Jessica Davis, Senior Editor, Enterprise Apps, Informationweek

Jessica Davis has spent a career covering the intersection of business and technology at titles including IDG's Infoworld, Ziff Davis Enterprise's eWeek and Channel Insider, and Penton Technology's MSPmentor. She's passionate about the practical use of business intelligence, predictive analytics, and big data for smarter business and a better world. In her spare time she enjoys playing Minecraft and other video games with her sons. She's also a student and performer of improvisational comedy. Follow her on Twitter: @jessicadavis.

Success Secrets of Top Omnichannel Retailers

It's a tough and changing environment for retailers. Yet some are enjoying continued success during turbulent times. We take a closer look at how they do it.

A2 Radio: Lean Analytics for 2018

Lean Analytics author Alistair Croll joins AllAnalytics radio to talk about how to apply the process for 2018.


Re: artificial intelligence
  • 7/4/2017 10:19:53 AM
NO RATINGS

@Lyndon: It may not be so much "extra" as "unique". They were once top of the market for secure mobile-device technology. Perhaps there is something there that they have been able to leverage.

Re: artificial intelligence
  • 7/3/2017 7:11:23 AM
NO RATINGS

..

Joe writes

The connected-vehicle vertical is one of the fastest-growing and most demanding segments when it comes to enterprise tech -- having enormous demands in connectivity, AI, IoT tech, and the like. Good on Blackberry for recognizing this and pivoting accordingly.

I'd have to wonder whether they've found something that gives them an extra edge in this technology. It would seem to me that they are facing some robust competition in this field, e.g. Apple, Google, Samsung, LG, etc.

..

Re: artificial intelligence
  • 7/3/2017 5:39:49 AM
NO RATINGS

@Lyndon: The connected-vehicle vertical is one of the fastest-growing and most demanding segments when it comes to enterprise tech -- having enormous demands in connectivity, AI, IoT tech, and the like. Good on Blackberry for recognizing this and pivoting accordingly.

Of course, to what degree it will work out for them ultimately, we'll see.

Re: artificial intelligence
  • 7/2/2017 7:40:35 PM
NO RATINGS

..

Joe writes "Well, Blackberry is still around -- alive and kicking. It's not doing nearly as well as it once was, however."

Blackberry is still in the game, but just barely. According to a June 7th Reuters report, the company "has developed new software for running complex computer systems on vehicles, giving the once dominant smartphone maker a leg up in a burgeoning segment of the technology market."

..

Re: artificial intelligence
  • 7/1/2017 1:00:41 PM
NO RATINGS

@maryam: Well, Blackberry is still around -- alive and kicking. It's not doing nearly as well as it once was, however.

Re: artificial intelligence
  • 7/1/2017 12:59:58 PM
NO RATINGS

@Predictable: Your comments remind me of when I attended the 2016 MIT Sloan CIO Symposium. One of the speakers compared the biggest companies of decades ago to how they were doing today and large market cap companies of today. The fastest growing companies, the speaker's chart showed, had far less in the way of employees and owned investment -- using examples like Uber and Airbnb.

Today, we're in the platform economy. Network effects make for big bucks, and the companies that can best deploy and leverage platforms can succeed whatever their CAPEX.

Moreover, to speak of your example with Google and Amazon, those are two great examples of companies that have elevated "pivoting" to such a level that they're just pretty much in (or able to be in) just about every business that there is -- and it somehow makes sense for them to be there.

Re: artificial intelligence
  • 7/1/2017 12:56:05 PM
NO RATINGS

@rbaz: Absolutely true. At the same time, capital doesn't just fall from the sky. Google wouldn't have it if it didn't have at least some idea of what it was doing as a business.

Re: artificial intelligence
  • 6/30/2017 11:37:17 PM
NO RATINGS

Too many companies are managed like it's still 1917 when slow decisions were best because significant capital was required for every initiative.

Google and Amazon are successful because they recognize the importance of moving quickly. The ability to make decisions fast is something every capital-starved start-up will use to attack the big players. Google and Amazon aren't leaving anything to chance.

Re: artificial intelligence
  • 6/30/2017 10:50:35 PM
NO RATINGS

(may they rest in peace) 

I'm certain it seemed like a great idea to focus on being great in one area only. It was great for a time, but didn't last.

Re: artificial intelligence
  • 6/29/2017 1:08:52 PM
NO RATINGS

Exactly and it seems that the ones that stick to only one core competency are destined to fail in today's world remember Palm and Blackberry --they were gone in an instant.

Page 1 / 4   >   >>
INFORMATION RESOURCES
ANALYTICS IN ACTION
CARTERTOONS
VIEW ALL +
QUICK POLL
VIEW ALL +