In an effort to keep up with its competitors in the fields of search and AI, Apple has hired John Giannandrea, Google’s head of search and artificial intelligence.
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An Apple as serious about AI as it was about silicon could be a powerful force for the future of services.
Apple has hired John Giannandrea, former Senior Vice President of Engineering, specifically search and Artificial Intelligence, at Google. In his new role running machine learning and AI strategy, Giannandrea will be reporting directly to Tim Cook, Apple’s CEO.
The bombshell news was shared with Apple employees in a company email, first reported on by The New York Times:
“Our technology must be infused with the values we all hold dear. John shares our commitment to privacy and our thoughtful approach as we make computers even smarter and more personal.”
AI is critical to the next great leap forward in computing. Ethical AI is critical to how we, as people, survive that leap.
Facebook has increasingly come under fire for how it does and could use AI to monetize and manipulate its users for advertising and influence peddling. Google, which has made a more public effort to balance deep insight with deep exploitation, still has to wrestle with what could happen when and if those two streams mix accidentally or maliciously at the expense of its users.
Apple, which makes its money selling customers goods and services rather than selling customers’ attention and insight, could offer an important alternative when it comes to fielding everything from intelligent assistants to autonomous technologies.
Yet Apple’s commitment to world-class AI has been called into question for years. Despite fielding AI at the silicon level with the 2017 A11 Bionic chipset, and using it to power features like Face ID, the company’s ground-breaking Siri personal assistant is widely seen as having fallen behind offerings from Google and Amazon, especially when it comes to consistency and third-party support.
How Giannandrea will change this remains to be seen.
Siri has recently moved from Eddy Cue’s internet services organization to Craig Federighi’s software engineering organization at Apple. Still, myriad features and teams exist across the orgs, as do other AI initiatives like Core ML, which includes computer vision, Face ID, and the autonomous future Tim Cook has spoken about.
I’ve been hoping for a while that Apple would unify AI services as its own organization, similar to how it grew out silicon separately from hardware engineering. With Giannandrea reporting to Cook, perhaps he can do for services what johny Srouji has done for hardware techhnologies.
No pressure.
Apple has hired Google’s former chief of search and artificial intelligence. He will be in charge of Apple’s machine learning and A.I. technologies.
According to a new report from The New York Times, Apple has hired John Giannandrea, Google’s now-former chief of search and artificial intelligence. He will reportedly take over the company’s efforts in machine learning and A.I.
Apple said on Tuesday that Mr. Giannandrea will run Apple’s “machine learning and A.I. strategy,” and become one of 16 executives who report directly to Apple’s chief executive, Timothy D. Cook.
Perhaps sensing the concern some might have for an artificial intelligence expert from Google joining the famously privacy-focused company, Tim Cook seemed to quell concerns in an email to Apple employees:
“Our technology must be infused with the values we all hold dear. John shares our commitment to privacy and our thoughtful approach as we make computers even smarter and more personal.”
Apple’s balance between customer data protection and advances in A.I. and machine learning is a common topic among pundits. The company has been criticized for not placing enough focus on advances in artificial intelligence. Hiring the guy in charge of A.I. at a company that regularly receives props for its A.I. and machine learning features is quite the move.
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The Next Web
One of the biggest challenges in astronomy is also the most obvious: space is big and it takes a long time to look at it all. This is why artificial intelligence has been such a boon to this science. It turns out that the same machine vision tools developed for tasks like guiding self-driving cars are also perfect for sorting through vast amounts of astronomical data. So much so, that astronomers announced this month that they’d used AI to find 6,000 new craters on the Moon.
Now, this isn’t that significant in itself. The Moon is estimated to have hundreds of thousands of craters, mostly caused by impacts with asteroids and meteors. This is because of a few factors. First, because the Moon has no atmosphere these objects have a free path…
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In the age of Digital, there is still enough room for the physical.
It is clear that enterprises wish to bring the digital user experience into the branch or office. Today, the mobile and digital banking experience is already part of our visit to the branch. Banks are digitizing the customer experience in the branch by placing tablets, reducing the paperwork and therefore, also the time we are spending there (we all like an instant experience, right?). The need to ensure a perfect experience in this competitive reality is receiving prime location. In my journey to help digital enterprises with perfecting their user experience, I see a growing need from not only bankers and insurance agents that are now more dependent on their tablets but also from pilots, aircraft technicians and others who work in the field. They are using internal apps and services but with the criticality of these services, it does not leave room for mistakes or glitches.
Marketing forces have recently introduced a new model that combined the Digital & Physical aspects of targeted exposure: Cost-Per-Visit. This new business metric aiming to encourage in-store consumption of services/goods and for brands to pay publishers for ads only when a customer is being exposed to it while visiting a specific location. In other words, using the CPV model not only helps brands increase foot traffic and boost sales, but also helps foster a more trusting relationship between brands, agencies and vendors.
So now that it’s clear that digital enterprises that have physical branches are placing greater emphasis on location intelligence, the question is how do you ensure quality on all different locations?
Proximity advertising is a good example, but how about or more basic one: searching the right branch? Does your service have the right ingredients to allow location based search (e.g. find the close branches to my current location, etc.)? Let’s assume the answer is yes – how would you test that nationwide assuming you’re representing a bank with branches nationwide? Covering such use case, would be a trivial part of a standard daily/weekly regression cycle, right? The answer in most cases would be the ability to inject the location intelligence as part of the automation suite. I often encounter trivial use cases while working with Digital Enterprises to optimize their UX:
This is a real example where a customer is continuously testing the availability of services in critical locations and conditions.
2. Network condition testing:
3. Location testing on steroids:
As described in a previous blog – patterns are becoming more and more important.
The future of location based testing will likely include the implementation of smart intelligence and interactions with IoT (including BLE devices, other smart terminals and POS).
The market is already heading in this direction: by analyzing historical location data and detailed behavioral patterns, digital enterprises can gain comprehensive insights into consumer preferences and habits which can be used for hyper-targeted campaigns and other engagement models.
Remember, the key for perfecting the experience is to BE IN THE RIGHT PLACE AT THE RIGHT TIME.
Earlier this week, IBM announced a new partnership with Apple, explaining how it would be adding IBM Watson Services to Core ML. Since Watson has already proved its prowess on Jeopardy, most folks know what Watson is. Core ML is probably less familiar: It’s Apple’s machine learning framework for the company’s software platforms. Specifically, Apple says that Core ML can be used with Siri, Camera and QuickType.
That reference of Siri jumps out at me. Granted, the IBM-Apple news is really geared towards building apps for enterprises — Apple and IBM have been partners in this area for a few years now — but I’m thinking ahead on other areas where Watson could could benefit Apple products. And Siri surely needs help, especially inside the Apple HomePod.
How so? Well, let’s step back a minute and see how Watson works today.
This video provides a fantastic explanation but if I had to summarize it, here’s how I see it. Watson ingests large amounts of unstructured data, most of which today is written information. After analyzing that data for patterns, Watson attempts to structure it to understand both the content and intent of any actions taken upon that data.
This is far more advanced than simply scanning a never-ending stream of question-answer pairs because not every question is asked the same way and that can change the meaning of the question or analysis. There’s certainly more to Watson than my limited interpretation, but these are the parts most relevant to my thought process.
So what if the unstructured data was human behavior in a smart home? Theoretically, Watson could determine both the context and the intent of users of that home and through pattern recognition, possibly anticipate the needs of people in the home from such insights. This is the autonomous level of smart homes that I alluded to last week when discussing routines and automations.
To be more specific, Watson could help make sense of all of the actions we take in, around and near our homes: When we generally wake, leave for work, what we cook and when, who comes and goes, when do we sit down to relax and what do we typically do during that time. For a home to be semi-autonomous, certain patterns need to be recognized from these actions. And those patterns can be combined with already available verification data such as GPS location, network traffic from Netflix or music from an online streaming service.
At that point, a digital assistant such as Siri can begin to anticipate things and make insightful suggestions without any programming or user configuration; two items used today for routines and automation.
For example, I typically retire to the home office at some point after dinner but I don’t do work. Instead, I turn a light on to read a book or watch TV and I may play some low-volume music. Now imagine if Siri knew that, thanks to Watson.
I might head upstairs to the office and find the light already turned on to my preferred brightness. Siri could proactively ask if I wanted to catch up on the show I most recently watched on Netflix. Perhaps I respond and say, “No thanks, I’m going to read for a while.” Maybe Siri prompts to see if I want music that’s tailored for light background noise while I read. You see where I’m going.
If it sounds impossible that such patterns could be detected or useful, think about Google Drive. Using its own machine learning, Google knows when I typically return to specific documents and it highlights them at the appropriate time. Think context and intent here.
A perfect example is when Stacey and I collaborate on the IoT Podcast show notes. The day and time of that effort varies but I’d say that 90% of the time, I open up Google Drive to add topics for the next show, the spreadsheet appears above my Drive contents in the “Quick Access” area. In fact, under the document, it says, “You usually open this Sheet around this time.” It’s a simple example of pattern recognition, but it’s also a powerful one.
If Google can do this with Drive documents and Watson can do this with unstructured, written data, it’s just a matter of doing the same thing with a different type of data: Objects and their actions in the smart home. There’s no guarantee that Apple is working with IBM on this to make Siri a smarter digital assistant in the home, but if they’re not, I think they should be.
Stacey on IoT | Internet of Things news and analysis
Peter Thiel’s data mining company Palantir Technologies has won a US Army contract to develop an intelligence platform that will replace an aging system that the Army currently used to collect and disseminate information, according to Bloomberg Technology.
The Army will pay Palantir and defense contractor Raytheon $ 876 million over the next decade to develop a replacement for the Distributed Common Ground System, says Bloomberg. That system is used by the various branches of the US military to interpret intelligence from a variety of platforms “spanning all echelons from space to mud,” according to the US Army.
Battlefields are complicated areas, and since the 1991 Gulf War in Iraq, the military has grappled with the need to take all…
