Machine Intelligence for Mobile Advertising and the Pros and Cons of Empathy (#wic)
On machine intelligence for mobile advertising, meeting up at the Re-Work Tech Summit London, the (un)usefulness of empathy, deep linking, and wearables and data protection. Comes with obligatory Apple Watch mention.
This Week in Context
Your Weekly Update on All Things Context, September 12 2014
Learn about emotion-aware mobile ad tech on this Q&A with Frank Verbis on the Argus Labs blog. Frank assisted in creating one of the first RTB platforms and is co-founder of the Strike4 investment fund. Read the full interview here.
If you’re a Londoner and interested in AI for advertising & crafting better customer experience, our CEO our founder will be representing us at the Re-Work Technology Summit London next week. Tweet @sentiance or mail firstname.lastname@example.org to meet up.
“On privacy, as a user, I will stop sharing data if they give nothing in return. Towards advertising this means I expect them to deliver a better service. They should become more like an emphatic advisor, don’t just throw anything at me at any possible moment in time. They need to understand my interests and habits and timing without me having to carry a webcam on top of my head.”
The Apple Watch & iPhone 6
In case you are not suffering from iAnnouncements overdose yet, Business Insider has a look at the ‘fitness value’ (priority for 30% of smart watch buyers) of the Apple watch versus the Basis wearable, and – for something lighter – this ‘iPhone 6 users: welcome to 2012!’ spec comparison will surely make you chuckle.
The Future Of Machine Intelligence In Mobile Advertising: Building Trust Between Consumers And Their Mobile Devices
“A lot of the current methods of advertising and marketing on mobile services fail in their execution because consumers ignore the adds – it’s obvious the content they are viewing is advertising—and either it’s completely irrelevant, or they’ve been provided with so much ‘spam’ in the past that they have learned to tune out advertising messaging,” adotas.com writes. Machine intelligence filters and algorithms can solve the underlying issue behind this only remotely relevant spam, by knowing what and when your message is relevant to the consumer. Helpful advertising in small doses can then have a far larger impact, both on consumers’ appreciation of your brand, and on your CTRs. (via Frank Verbist)
At Argus Labs, we’re working towards the era of empathic devices, which makes this editorial – and claim – by Paul Bloom more than relevant: “If you want to be good and do good, empathy is a poor guide”. Bloom explains the differences between emotional and cognitive empathy, the link with moral values, and asks just how much empathy we really want in ourselves, our children, our friends, and our society. Well worth the read, even if you end up disagreeing.
On Deep Linking, Mobile Advertisements & Search
On the SearchBlog, John Battelle shares his ‘early lessons from his mobile deep dive‘. Or how re-engagement ads open the door to new topologies (and economics) across mobile, how the app store’s days are limited, and how search is the key to this all.
Wearable Tech and the data protection problem
Earlier this year, the UK’s Information Commissioner’s Office published a blog highlighting how easy it is to collect personal data using wearable technology. This was one of the first times the ICO had clarified its position on wearable technology, using the post to reinforce the fact that as soon as personal data is used for business purposes it becomes subject to the Data Protection Act 1998. How are companies coping with the governance, risk and compliance landscape they are facing now, and are their BYOD strategies ready for wearables? (via Bavo)
Cats, Algorithms & Art
The Cat or Human art installation presents cats misidentified by an algorithm (OpenCV) as human, and humans that an algorithm (KITTYDAR) thought were cats.
Papers, Talks & Research
- Sequence to Sequence Learning with Neural Networks (deep neural networks, machine translation, paper) – One of the authors, Ilya Sutskever, discusses it on Google Plus here.
- AI Evaluation: past, present and future (artificial intelligent, the AI effect, paper)
- Emotion and Disposition Detection in Medical Machines: Chances and Challenges (ethics, medical devices, abstract)
- Automatic Personality Estimation (speech, personality detection, abstract)
- Using Hashtags as Labels for Supervised Learning of Emotions in Twitter Messages (machine learning, emotion detection, poster)
Enjoy the weekend!
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