AI drive spawns new takes on chip design

As soon as we solve machine
learning we will fix printer.

It is has been interesting to see a re-mergence in interest in new chip architectures. Just when you think it is all been done and there's nothing new. Bam! For sure.

The driver these days is A.I. but more particularly the machine learning aspect of AI. GPUs jumped out of the gamer console and onto the Google and Facebook data center. But there was more in the way of hardware tricks to come. The effort is to get around the tableau I here repeatedly cited: the scene is the data scientist sitting there thumb twiddling while the neural machine slowly does its learning.

I know when I saw that Google had created a custom ASIC for Tensor Flow processing, I was taken aback. If new chips are what is needed to succeed in this racket, it will be a rich man's game.

Turns out a slew of startups are on the case. This article by Cade Metz suggests that at least 45 startups are working on chips for AI type applications such as speech recognition and self-driving cars. It seems the Nvidia GPU that has gotten us to where we are, may not be enough going forward. Co processors for co processors, chips that shuttle data about in I/O roles for GPUs, may be the next frontier.

Metz names a number of AI chip startups: Cerbras, Graphcore, Wave Computing, Mythic, Nervana (now part of Intel). - Jack Vaughan

Related
https://www.nytimes.com/2018/01/14/technology/artificial-intelligence-chip-start-ups.html

What is the risk of AI?

Happy New Year from all of us at the DataDataData blog. Let's start the year out with a look at Artificial Intelligence - actually a story thereof. That is, "Leave Artificial Intelligence Alone" by Andrew Burt, appearing in last Friday's NYTimes' Op-Ed section.

Would that people could leave AI alone! You cant pick up a the supermarket sales flyer without hearing someone's bit on the subject. As Burt points out, a lot of the discussion is unnecessarily - and unhelpfully - doomy and gloomy. Burt points out that AI lacks definition. You can see the effect in much of the criticism, which lashes out with haymakers at a phantom - one that really comprises very many tributary technologies - quite various ones at that.

Some definition, some narrowing of the problem scope is in order.

If you study the history of consumer data privacy you discover, as Burt reminds, the Equal Credit Opportunity Act of 1974. Consider it as a pathway for data privacy that still can be followed.

Burt also points to SR 11-7 regulations that are intended to provide breadcrumbs back to how and why trading models were constructed, so that there is good understanding of risk involved in the automated pits of Wall Street.

Within the United States’ vast framework of laws and regulatory agencies already lie answers to some of the most vexing challenges created by A.I. In the financial sector, for example, the Federal Reserve enforces a regulation called SR 11-7, which addresses the risks created by the complex algorithms used by today’s banks. SR 11-7’s solution to those challenges is called “effective challenge,” which seeks to embed critical analysis into every stage of an algorithm’s life cycle — from thoroughly examining the data used to train the algorithm to explicitly outlining the assumptions underlying the model, and more. While SR 11-7 is among the most detailed attempts at governing the challenges of complex algorithms, it’s also one of the most overlooked.

Burt sees such staged algorithm analysis as a path for understanding AI and machine learning going forward.

It is good to see there may be previous experience that can be tapped when looking at how to handle AI decision making - as opposed to jumping up and down and yelling 'the sky is falling.'

As he says, it is better to distinguish the elements of AI application according to use cases, and look at regulation specifically in verticals - where needed. 

Spoke with Andrew Burt last year as part of my work for SearchDataManagement - linked to here: Machine learning meets Data Governance.  - Jack Vaughan

Paradise Graph Papers

The Paradise Papers files expose offshore holdings of political leaders and their financiers as well as household-name companies that slash taxes through transactions conducted in secret. Financial deals of billionaires and celebrities are also revealed in the documents. 1.4 TB of data – 13.4 million documents – includes information leaked from trust company Asiaciti and from Appleby, a 100-year-old offshore law firm specializing in tax havens as well as information leaked.more to come


Related

https://www.icij.org/investigations/paradise-papers/about/

https://neo4j.com/blog/neo4j-power-behind-paradise-papers/

https://neo4j.com/blog/depth-graph-analysis-paradise-papers/

https://linkurio.us/blog/big-data-technology-fraud-investigations/

Does data make baseball duller?

Let's not talk quality of life and data, lets talk baseball and data. Moneyball was an eye opener in the rise of big data analytics as a popular meme. And why not? It had Brad Pitt. Well the movie did. It showed a guy thinking outside of the box could re-imagine the game. The hell with 'he looks like a ball player' hello to can he take a walks? For a small market team - a tonic. But now we are seeing a great downside of worshiping at the altar of data: Really boring baseball. Removing too many pitchers too soon...Embracing strikeouts...Avoiding ground ball and liner hits...focus on homer... Still one wonders if some of these move do auger obvious counter moves for those outside of box thinkers of today... in the face of elaborate boring shifts... why not bunt?

https://www.wsj.com/articles/the-downside-of-baseballs-data-revolutionlong-games-less-action-1507043924

Forensic analytics

While at Bell Labs in the 1980s, Dalal said, he worked with a team that looked back on the 1986 Challenger space shuttle disaster to find out if the event could have been predicted. It is well-known that engineering teams held a tense teleconference the night before the launch to review data that measured risk. Ultimately, a go was ordered, even though Cape Canaveral, Fla., temperatures were much lower than in any previous shuttle flight. A recent article looks at the issues with an eye on how they are related to analytics today.

http://searchdatamanagement.techtarget.com/opinion/Making-connections-Big-data-algorithms-walk-a-thin-line

Working notes - The profession of data

Working - The profession of data The software profession took clear steps forward during the 1960s. Software had become essential to U.S. military defense, not to mention IBM, and, with the imperative to go to the Moon, there appeared money and interest enough for methodologists to ponder what type of professional processes could lead to predictable, successful and repeatable outcomes.

More

https://moontravellerherald.blogspot.com/2017/09/data-science-and-dev-ops-thoughts.html

I've Seen the Future of Graphed Data

https://linkurio.us/about/
https://linkurio.us/solution/neo4j/
https://linkurio.us/blog/cerved-financial-networks/

Data, like people can lie

Three cheers for the West Virginia University team whose research caused the net to enclose upon the existential Euro varmints of VW. Who used software to neuter U.S. emission tests (and probably laugh about it over bears). Data, like people can lie. Data exists within the construct of the civilization around it -  What's the bet that a few Euro's in an off shore bank will cause Trump and Bannion to cut research funding in to emissions? - Jack Vaughan

https://www.nytimes.com/2017/05/06/business/inside-vws-campaign-of-trickery.html

It couldnt look bleaker unless your name is Meeker

Mary Meeker's annual report for Kleiner Perkins on the status of Internet commerce is always interesting - chock full of data and packed with gleefully greedy West Coast VC perspective.  Let's look at some highpoints out of the 150-plus Power Point Slide opus.

Do you smell the fear in the Fortune 500? Smells like they could use some baby wipes. They can get them from Amazon, actually, which trails only Huggies and Pampers for online market share. For Duracell, it is deep doodoo, as Amazon surpasses the check out counter champ entirely  - on the Web. All that marketing and technology innovation - not too mention shelf shoving -- over many years seems for little or naught. (Off beat: I worked for 6 months at a drug store on 34th St in the 1970s and among the thing I learned was: "You cannot keep Pampers on the shelf" Translation: Shit happens.)

The sound of foot prints echoes double in network television where the biggies are flat or in decline, but Netflix is on a skyrocket up.

And disruptors (the Internet advertising vehicles  that disrupted convention media) can be disrupted too, especially if they face big hungry disruptors  such as Facebook and Google. They who grow ad revenue in double digits while Everybody Else flatly contests the small pie leftovers.

Maybe Facebook and Google are as much beneficiaries of an underlying sea change in Internet usage..as of anything else. While desktop and Laptop Internet use has been steady or in slight decline over the last eight years, Internet   time on the smartphone side has been vaulting forward stridently. What is different about mobile? The message might be real real concise, the ambiance more transactional, and the market more consumerish.

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Related
http://www.kpcb.com/internet-trends