After the recent Google news (data of half a million people compromised, new hardware — Pixel 3, new tablet) broke my brain… I needed to have a semi-coherent venting / ranting session. This is me. Taking a break during production of some other videos. And venting about Google. DRM-Free MP3/MP4 Downloads here: https://www.patreon.com/posts/21965685
Well protected? The FBI has been increasingly outspoken in its opposition to ubiquitous encryption of Apple and Google products. Photograph: Alamy
Apple, Google, other tech giants and a number of noted cryptologists have signed a letter to the Obama administration urging the US government to preserve strong encryption against pressure from law enforcement and surveillance agencies.
The letter argues that “strong encryption is the cornerstone of the modern information economy’s security,” and that the government should “fully support and not undermine efforts to create encryption standards [nor] in any way subvert, undermine, weaken or make vulnerable” commercial software.
It was obtained by the Washington Post in advance of its publication on Tuesday. The letter is also signed by three members of Obama’s five-person review group set up in 2013 to reassess technology policy in the wake of Edward Snowden’s leaks that summer.
Richard A Clarke, one of the review group signatories, made a comparison to a failed attempt to institute back doors in the phone network in the 90s. “If they couldn’t pull it off at the end of the cold war, they sure as hell aren’t going to pull it off now,” he told the newspaper.
Law enforcement agencies such as the FBI have been increasingly outspoken in their opposition to ubiquitous encryption. In October, the bureau’s director, James Comey, slated the decision of Apple and Google to turn on encryption by default.
With the launch of iOS 8, “the information stored on many iPhones and other Apple devices will be encrypted by default”, Comey told the Brookings Institute in Washington DC last year. “Shortly after Apple’s announcement, Google announced plans to follow suit with its Android operating system. This means the companies themselves won’t be able to unlock phones, laptops and tablets to reveal photos, documents, email and recordings stored within.”
Comey said: “At the outset, Apple says something that is reasonable – that it’s not that big a deal … Apple argues, for example, that its users can back up and store much of their data in ‘the cloud’ and that the FBI can still access that data with lawful authority. But uploading to the cloud doesn’t include all of the stored data on a bad guy’s phone, which has the potential to create a black hole for law enforcement.”
In response to such fears, many, including the British prime minister, David Cameron, have suggested that firms could build back doors in to their encryption – special weaknesses which allow law enforcement and surveillance agencies to break into otherwise secure connections. But the security industry is adamant that such back doors are technologically infeasible.
Due to the nature of modern encryption, “there is no way to put in a back door or magic key for law enforcement that malevolent actors won’t also be able to abuse”, argues the Electronic Frontier Foundation’s Jeremy Gillula.
Tech More: Google Amazon
The Single Most Terrifying Trend Facing Google
Two and a half years ago we wrote a post headlined “Forget Apple, Forget Facebook: Here’s The One Company That Actually Terrifies Google Execs.”
That company? Amazon.
Google is a search company, but the searches it makes money from are the searches people do before they are about to buy something online.
These commercial searches make up about 20% of total Google searches. Those searches are where the ads are.
Two and a half years ago we wrote, “What Googlers worry about in private is a growing trend among consumers to skip Google altogether, and to just go ahead and search for the product they would like to buy on Amazon.com, or, on mobile in an Amazon app.”
We noted that, according to ComScore, “the trend is real.” Searches on Amazon.com were up 73% year over year.
Well, we checked back with ComScore recently, and the news remains bad for Google. Desktop search queries on Amazon increased 47% between September 2013 and September 2014, according to ComScore.
Even worse for Google, that number doesn’t tell the whole story.
In the past two and a half years, the number of mobile internet users surpassed desktop internet users.
desktop versus mobile users in 2014Comscore
On mobile, using Google as a starting point when you want to buy something makes even less sense.
Think about it. Why go through these steps?
Open your web browser on your phone.
Google search “bike gloves.”
Scan some text links.
Click on a link to go to a product page at some e-commerce store.
Click to add the item to your cart.
Input your credit-card info.
Type in your address.
Select the shipping preferences you want to pay for.
When you can just …
Open the Amazon app on your phone.
Search “bike gloves.”
Click one button to buy the product with your usual credit card, and have it shipped to your usual address free.
Two and a half years ago, we wrote that Google’s Amazon nightmare would get scarier if Amazon’s Kindle Fire tablets and (rumored) phones ever got wide adoption.
That hasn’t happened yet. Kindle Fire sales are pretty bad. But earlier this month, Amazon CEO Jeff Bezos made it clear in an onstage interview at our BI Ignition conference that he’s not giving up on the project.
Bad news for Google execs trying to get eight hours a night.
Nicholas Carlson is the author of “Marissa Mayer and the Fight to Save Yahoo!”
From natural language processing to image recognition, there are a variety of technologies, each suited to different purposes, comprising today’s smart machines landscape, CIO Journal Journalist Thomas H. Davenport writes. Maybe some time we will have a system to recommend the best smart machine technology for the desired application. Until then “smart humans” have a role to play.
ut what specific technologies are we dealing with here, and what the heck do we call them? “Artificial intelligence” has been bandied about for a while, and it’s become an umbrella term for a lot of different tools. Perhaps its only problem is that it’s old, and we have heard so many times of its rise (and fall) that many have grown weary and skeptical of the term.
The newest umbrella term is “cognitive computing,” suggesting that we are finally developing computers that can mimic the human brain. The only problem with this term comes when you have an extended conversation with a neuroscientist, and you realize that we still know very little about how the brain works. There are articles attesting to our ignorance here and here. So to refer to these smart machines as examples of cognitive computing is not really very smart.
The truth is that there are a variety of technologies that comprise the current landscape of smart machines (the overview term that I find least problematic). Each is suited to a different set of purposes, and it’s pretty rare that more than one is integrated in a particular application. Unlike the human brain—which can perform a variety of cognitive tasks, a particular computer system can only do one type of task. Here’s an incomplete alphabetical list of the systems that can undertake cognitive tasks:
Analytics—Tools that do mathematical or statistical analysis on structured data—typically numbers. These have grown in power and sophistication over recent years, and can now be used to drive a variety of decision types. They are also increasingly embedded in other systems and business processes.
Complex event processing—This type of system takes as inputs a variety of real-time data sources and types about events, and then combines them to determine their significance or to take action. It takes in data, transforms it as necessary, analyzes it to detect trends and patterns, and takes necessary actions. CEP systems are widely used in algorithmic stock trading, in which a system might monitor a variety of economic and social indicators, and then determine that a stock trade would be economically beneficial. CEP is also used to detect credit card fraud—ideally before it is successfully committed.
Image recognition—Early systems for recognizing images were quite limited. But now that computers have become a lot more powerful, they can identify more complex images, including specific faces and types of animals. Google was able, for example, to build a complex image recognition that could identify cats in videos.
Machine learning/neural networks—These are somewhat automated approaches to analytics. They create models to fit data and improve as they learn. There are various forms of machine learning approaches, including neural networks, Bayesian classifiers, decision trees, support vector machines, and so forth. The differences between these are generally perceptible only to specialists.
Natural language processing—These tools take text or speech as input, and increasingly can “read” or extract meaning from it. IBM Corp.’s original Watson falls into this category, as does Apple Inc.’s Siri. Since much of human experience is represented in language, this is a powerful category, and the one most likely to be described as “cognitive computing.”
Rules and business rules–Rules express logic in a structured language—typically an “if/then” structure. They were the primary programming approach for so-called “expert systems,” a branch of artificial intelligence. Business rules express operational approaches to business in this structure. A business rule might specify how customers are to be treated (e.g., a customer returning an item doesn’t have to go through a credit check), or when certain quantitative thresholds are reached (e.g., a mortgage loan can be given if the loan-to-value ratio for the house in question is less than 20%).
These and other categories of smart machines can now address almost any topic on which there is data or recorded expertise. They’re all useful, but they’re not universally useful. Since each tool is suited only to a particular purpose, managers increasingly need to be familiar with the tools and how they fit particular applications. If you’ve got a problem that can be structured in a set of rules, you don’t want to hire Watson for that job. If you have a bunch of data in rows and columns, a rules engine won’t help you much.
One thing that human brains—at least some of them—are good at is seeing the big picture. That can include looking over the variety of technology alternatives for cognitively-oriented situations, and selecting the right one. We could probably have a system—I am envisioning a set of rules—that asks the business user a set of questions about the desired application, and then recommends a particular technology. But we don’t have that yet. So in the short run we will have to rely not on smart machines, but on smart humans, for this purpose.
Thomas H. Davenport is a Distinguished Professor at Babson College, a Research Fellow at the Center for Digital Business, Director of Research at the International Institute for Analytics, and a Senior Advisor to Deloitte Analytics.
All You Can Eat Distraction!
Writer, scribbler, obsessive maker of notes. Andy has written TV sketches, appeared on the BBC dressed as The Elephant Man, and is regularly shouted at by drunks for his choice of wardrobe.
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